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Federated learning for 6G-enabled secure communication systems: a comprehensive survey

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Abstract

Machine learning (ML) and Deep learning (DL) models are popular in many areas, from business, medicine, industries, healthcare, transportation, smart cities, and many more. However, the conventional centralized training techniques may not apply to upcoming distributed applications, which require high accuracy and quick response time. It is mainly due to limited storage and performance bottleneck problems on the centralized servers during the execution of various ML and DL-based models. However, federated learning (FL) is a developing approach to training ML models in a collaborative and distributed manner. It allows the full potential exploitation of these models with unlimited data and distributed computing power. In FL, edge computing devices collaborate to train a global model on their private data and computational power without sharing their private data on the network, thereby offering privacy preservation by default. But the distributed nature of FL faces various challenges related to data heterogeneity, client mobility, scalability, and seamless data aggregation. Moreover, the communication channels, clients, and central servers are also vulnerable to attacks which may give various security threats. Thus, a structured vulnerability and risk assessment are needed to deploy FL successfully in real-life scenarios. Furthermore, the scope of FL is expanding in terms of its application areas, with each area facing different threats. In this paper, we analyze various vulnerabilities present in the FL environment and design a literature survey of possible threats from the perspective of different application areas. Also, we review the most recent defensive algorithms and strategies used to guard against security and privacy threats in those areas. For a systematic coverage of the topic, we considered various applications under four main categories: space, air, ground, and underwater communications. We also compared the proposed methodologies regarding the underlying approach, base model, datasets, evaluation matrices, and achievements. Lastly, various approaches’ future directions and existing drawbacks are discussed in detail.

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References

  • A.A. for everyone, We research and build artificial intelligence technology and services. https://sherpa.ai/

  • Abdel-Basset M, Moustafa N, Hawash H (2022) Privacy-preserved cyberattack detection in industrial edge of things (IEOT): a blockchain-orchestrated federated learning approach. IEEE Trans Ind Inform 8(11):7920–7934

    Google Scholar 

  • Ahmadi M, Ulyanov D, Semenov S, Trofimov M, Giacinto G (2016) Novel feature extraction, selection and fusion for effective malware family classification. In: Proceedings of the sixth ACM conference on data and application security and privacy, pp 183–194

  • Aich S, Sinai NK, Kumar S, Ali M, Choi YR, Joo M-I, Kim H-C (2021) Protecting personal healthcare record using blockchain & federated learning technologies. In: 2021 23rd international conference on advanced communication technology (ICACT), pp 109–112. IEEE

  • Aïvodji UM, Gambs S, Martin A (2019) Iotfla: a secured and privacy-preserving smart home architecture implementing federated learning. In: 2019 IEEE security and privacy workshops (SPW), pp 175–180. IEEE

  • Al-Marri NAA-A, Ciftler BS, Abdallah MM (2020) Federated mimic learning for privacy preserving intrusion detection. In: 2020 IEEE international black sea conference on communications and networking (BlackSeaCom), pp 1–6. IEEE

  • Ammad-Ud-Din M, Ivannikova E, Khan SA, Oyomno W, Fu Q, Tan KE, Flanagan A (2019) Federated collaborative filtering for privacy-preserving personalized recommendation system, arXiv preprint arXiv:1901.09888

  • Andreina S, Marson GA, Möllering H, Karame G (2020) Baffle: backdoor detection via feedback-based federated learning, arXiv preprint arXiv:2011.02167

  • Arachchige PCM, Bertok P, Khalil I, Liu D, Camtepe S, Atiquzzaman M (2020) A trustworthy privacy preserving framework for machine learning in industrial IoT systems. IEEE Trans Ind Inform 16(9):6092–6102

    Google Scholar 

  • Aramoon O, Chen P-Y, Qu G, Tian Y (2021) Meta federated learning, arXiv preprint arXiv:2102.05561

  • Bai Y, Fan M (2021) A method to improve the privacy and security for federated learning. In: 2021 IEEE 6th international conference on computer and communication systems (ICCCS), pp 704–708. IEEE

  • Banerjee S, Odelu V, Das AK, Chattopadhyay S, Kumar N, Park Y, Tanwar S (2018) Design of an anonymity-preserving group formation based authentication protocol in global mobility networks. IEEE Access 6:20673–20693

    Google Scholar 

  • Beaufays FS, Chen M, Mathews R, Ouyang T (2019) Federated learning of out-of-vocabulary words

  • Beguier C, Tramel EW (2020) Safer: sparse secure aggregation for federated learning, arXiv preprint arXiv:2007.14861

  • Blanco-Justicia A, Domingo-Ferrer J, Martínez S, Sánchez D, Flanagan A, Tan KE (2020) Achieving security and privacy in federated learning systems: survey, research challenges and future directions, arXiv preprint arXiv:2012.06810

  • Bonawitz K, Ivanov V, Kreuter B, Marcedone A, McMahan HB, Patel S, Ramage D, Segal A, Seth K (2017) Practical secure aggregation for privacy-preserving machine learning. In: Proceedings of the 2017 ACM SIGSAC conference on computer and communications security, pp 1175–1191

  • Bonawitz K, Eichner H, Grieskamp W, Huba D, Ingerman A, Ivanov V, Kiddon C, Konečnỳ J, Mazzocchi J, McMahan HB et al (2019) Towards federated learning at scale: system design, arXiv preprint arXiv:1902.01046

  • Bouacida N, Mohapatra P (2021) Vulnerabilities in federated learning. IEEE Access 9:63229–63249

    Google Scholar 

  • Brik B, Ksentini A, Bouaziz M (2020) Federated learning for UAVs-enabled wireless networks: Use cases, challenges, and open problems. IEEE Access 8:53841–53849

    Google Scholar 

  • Brüß C (2021) Federated learning in pedestrian trajectory prediction tasks, in Master Thesis, Lehrstuhl für Datenverarbeitung Technische Universität München

  • Canetti R, Feige U, Goldreich O, Naor M (1996) Adaptively secure multi-party computation. In: Proceedings of the twenty-eighth annual ACM symposium on theory of computing, pp 639–648

  • Cao D, Chang S, Lin Z, Liu G, Sun D (2019) Understanding distributed poisoning attack in federated learning. In: 2019 IEEE 25th international conference on parallel and distributed systems (ICPADS), pp 233–239. IEEE

  • Cao T-D, Truong-Huu T, Tran H, Tran K (2020) A federated learning framework for privacy-preserving and parallel training, arXiv preprint arXiv:2001.09782

  • Cetin B, Lazar A, Kim J, Sim A, Wu K (2019) Federated wireless network intrusion detection. In: 2019 IEEE international conference on Big Data (Big Data), pp 6004–6006. IEEE

  • Chai Z, Ali A, Zawad S, Truex S, Anwar A, Baracaldo A, Zhou Y, Ludwig H, Yan F, Cheng Y (2020) Tifl: a tier-based federated learning system. In: Proceedings of the 29th international symposium on high-performance parallel and distributed computing, pp 125–136

  • Chamikara MAP, Bertok P, Khalil I, Liu D, Camtepe S (2021) Privacy preserving distributed machine learning with federated learning. Comput Commun 171:112–125

    Google Scholar 

  • Chaudjary S, Kakkar R, Gupta R, Tanwar S, Agrawal S, Sharma R (2022) Blockchain and federated learning-based security solutions for telesurgery system: a comprehensive review. Turk J Electr Eng Comput Sci 30(7):2446–2488

    Google Scholar 

  • Chen M, Yang Z, Saad W, Yin C, Poor HV, Cui S (2019) Performance optimization of federated learning over wireless networks. In: 2019 IEEE global communications conference (GLOBECOM), pp 1–6. IEEE

  • Chen Y, Qin X, Wang J, Yu C, Gao W (2020a) Fedhealth: a federated transfer learning framework for wearable healthcare. IEEE Intell Syst 35(4):83–93

    Google Scholar 

  • Chen H, Li H, Xu G, Zhang Y, Luo X (2020b) Achieving privacy-preserving federated learning with irrelevant updates over e-health applications. In: ICC 2020-2020 IEEE international conference on communications (ICC), pp 1–6. IEEE

  • Chena B, Zenga X, Zhang W (2021) Federated learning for cross-block oil-water layer identification, arXiv preprint arXiv:2112.14359

  • Cheng K, Fan T, Jin Y, Liu Y, Chen T, Papadopoulos D, Yang Q (2021) Secureboost: a lossless federated learning framework. IEEE Intell Syst 36(6):87–98

    Google Scholar 

  • Chhikara P, Tekchandani R, Kumar N, Tanwar S, Rodrigues JJPC (2021) Federated learning for air quality index prediction using UAV swarm networks. In 2021 IEEE global communications conference (GLOBECOM), pp 1–6

  • Cirincione G, Verma D (2019) Federated machine learning for multi-domain operations at the tactical edge. In: Artificial intelligence and machine learning for multi-domain operations applications, vol 11006. International Society for Optics and Photonics, p 1100606

  • Dasari SV, Mittal K, Sasirekha G, Bapat J, Das D (2021) Privacy enhanced energy prediction in smart building using federated learning. In 2021 IEEE international IOT, electronics and mechatronics conference (IEMTRONICS), pp 1–6. IEEE

  • David L, Arús-Pous J, Karlsson J, Engkvist O, Bjerrum EJ, Kogej T, Kriegl JM, Beck B, Chen H (2019) Applications of deep-learning in exploiting large-scale and heterogeneous compound data in industrial pharmaceutical research. Front Pharmacol 10:1303

    Google Scholar 

  • Department WA. An industrial grade federated learning framework. FATE. https://fate.fedai.org/

  • Diao E, Ding J, Tarokh V (2020) Heterofl: computation and communication efficient federated learning for heterogeneous clients, arXiv preprint arXiv:2010.01264

  • Domingo-Ferrer J, Torra V (2005) Ordinal, continuous and heterogeneous k-anonymity through microaggregation. Data Min Knowl Disc 11(2):195–212

    MathSciNet  Google Scholar 

  • Domingo-Ferrer J, Martínez S, Sánchez D, Soria-Comas J (2017) Co-utility: self-enforcing protocols for the mutual benefit of participants. Eng Appl Artif Intell 59:148–158

    Google Scholar 

  • Domingo-Ferrer J, Blanco-Justicia A, Manjón J, Sánchez D (2021) Secure and privacy-preserving federated learning via co-utility. IEEE Internet Things J 9(5):3988–4000

    Google Scholar 

  • Dong Y, Chen X, Shen L, Wang D (2020) Eastfly: efficient and secure ternary federated learning. Comput Secur 94:101824

    Google Scholar 

  • Elbir AM, Soner B, Coleri S (2020) Federated learning in vehicular networks, arXiv preprint arXiv:2006.01412

  • Enthoven D, Al-Ars Z (2020) An overview of federated deep learning privacy attacks and defensive strategies, arXiv preprint arXiv:2004.04676

  • Fan Y, Li Y, Zhan M, Cui H, Zhang Y (2020a) Iotdefender: a federated transfer learning intrusion detection framework for 5g IoT. In: 2020 IEEE 14th international conference on big data science and engineering (BigDataSE), pp 88–95

  • Fan S, Xu H, Fu S, Xu M (2020b) Smart ponzi scheme detection using federated learning. In: 2020 IEEE 22nd international conference on high performance computing and communications; IEEE 18th international conference on smart city; IEEE 6th international conference on data science and systems (HPCC/SmartCity/DSS), pp 881–888. IEEE

  • Fang Q, Yu S, Chen X (2021) Olive branch learning: a novel federated learning framework for space-air-ground integrated network In: 2021 international conference on space-air-ground computing (SAGC), pp 44–50. IEEE

  • Federated T (2019) Machine learning on decentralized data, TensorFlow. https://www.tensorflow.org/federated. Accessed 13 Oct 2020

  • Feng S, Yu H (2020) Multi-participant multi-class vertical federated learning, arXiv preprint arXiv:2001.11154

  • Fereidooni H, Marchal S, Miettinen M, Mirhoseini A, Möllering H, Rieger TDNP, Sadeghi A-R, Schneider T, Yalame H, Zeitouni S (2021) Safelearn: secure aggregation for private federated learning

  • Fraboni Y, Vidal R, Lorenzi M (2021) Free-rider attacks on model aggregation in federated learning. In: International conference on artificial intelligence and statistics, PMLR, pp 1846–1854

  • Fredrikson M, Jha S, Ristenpart T (2015) Model inversion attacks that exploit confidence information and basic countermeasures. In: Proceedings of the 22nd ACM SIGSAC conference on computer and communications security, pp 1322–1333

  • Friha O, Ferrag MA, Shu L, Maglaras L, Choo K-KR, Nafaa M (2022) Felids: federated learning-based intrusion detection system for agricultural internet of things. J Parallel Distrib Comput 165:17–31

    Google Scholar 

  • Gâlvez R, Moonsamy V, Diaz C (2020) Less is more: a privacy-respecting android malware classifier using federated learning, arXiv preprint arXiv:2007.08319

  • Geiping J, Bauermeister H, Dröge H, Moeller M (2020a) Inverting gradients-how easy is it to break privacy in federated learning? Adv Neural Inf Process Syst 33:16937–16947

    Google Scholar 

  • Geiping J, Bauermeister H, Dröge H, Moeller M (2020b) Inverting gradients—how easy is it to break privacy in federated learning?, arXiv preprint arXiv:2003.14053

  • Ghosh A, Chung J, Yin D, Ramchandran K (2020) An efficient framework for clustered federated learning, arXiv preprint arXiv:2006.04088

  • Gong X, Sharma A, Karanam S, Wu Z, Chen T, Doermann D, Innanje A (2022) Preserving privacy in federated learning with ensemble cross-domain knowledge distillation

  • Gu B, Xu A, Huo Z, Deng C, Huang H (2021) Privacy-preserving asynchronous vertical federated learning algorithms for multiparty collaborative learning. IEEE Trans Neural Netw Learn Syst 33(11):6103–6115

    MathSciNet  Google Scholar 

  • Guo X, Liu Z, Li J, Gao J, Hou B, Dong C, Baker T (2020) V eri fl: communication-efficient and fast verifiable aggregation for federated learning. IEEE Trans Inf Forensics Secur 16:1736–1751

    Google Scholar 

  • Gupta R, Shukla A, Tanwar S (2020) Aayush: a smart contract-based telesurgery system for healthcare 4.0. In: 2020 IEEE international conference on communications workshops (ICC Workshops), pp 1–6

  • Gupta R, Nair A, Tanwar S, Kumar N (2021a) Blockchain-assisted secure UAV communication in 6g environment: architecture, opportunities, and challenges. IET Commun 15(10):1352–1367

    Google Scholar 

  • Gupta R, Kumari A, Tanwar S (2021b) Fusion of blockchain and artificial intelligence for secure drone networking underlying 5g communications. Trans Emerg Telecommun Technol 32(1):e4176

    Google Scholar 

  • Hai T, Zhou J, Srividhya S, Jain SK, Young P, Agrawal S (2022) Bvflemr: an integrated federated learning and blockchain technology for cloud-based medical records recommendation system. J Cloud Comput 11(1):1–11

    Google Scholar 

  • Han Q, Yang S, Ren X, Zhao P, Zhao C, Wang Y (2022) Pcfed: privacy-enhanced and communication-efficient federated learning for industrial iots. IEEE Trans Ind Inf 18(9):6181–6191

    Google Scholar 

  • Hard A, Rao K, Mathews R, Ramaswamy S, Beaufays F, Augenstein S, Eichner H, Kiddon C, Ramage D (2018) Federated learning for mobile keyboard prediction, arXiv preprint arXiv:1811.03604

  • He X, Chen Q, Tang L, Wang W, Liu T (2022) Cgan-based collaborative intrusion detection for UAV networks: a blockchain empowered distributed federated learning approach. IEEE Internet Things J

  • Hoofnagle CJ, van der Sloot B, Borgesius FZ (2019) The European union general data protection regulation: what it is and what it means. Inf Commun Technol Law 28(1):65–98

    Google Scholar 

  • Hsu R-H, Wang Y-C, Fan C-I, Sun B, Ban T, Takahashi T, Wu T-W, Kao S-W (2020) A privacy-preserving federated learning system for android malware detection based on edge computing. In: 2020 15th Asia Joint Conference on Information Security (AsiaJCIS), pp 128–136. IEEE

  • Hu R, Gong Y, Guo Y (2020) Cpfed: communication-efficient and privacy-preserving federated learning, arXiv preprint arXiv:2003.13761

  • Huba D, Nguyen J, Malik K, Zhu R, Rabbat M, Yousefpour A, Wu C-J, Zhan H, Ustinov P, Srinivas H et al (2022) Papaya: practical, private, and scalable federated learning. Proc Mach Learn Syst 4:814–832

    Google Scholar 

  • IBM. Ibm federated learning, https://ibmfl.mybluemix.net/

  • Iqbal R, Maniak T, Karyotis C (2019) Intelligent remote monitoring of parking spaces using licensed and unlicensed wireless technologies. IEEE Netw 33(4):23–29

    Google Scholar 

  • Iqbal R, Doctor F, More B, Mahmud S, Yousuf U (2020) Big data analytics and computational intelligence for cyber-physical systems: recent trends and state of the art applications. Futur Gener Comput Syst 105:766–778

    Google Scholar 

  • Islam A, Al Amin A, Shin SY (2022) Fbi: a federated learning-based blockchain-embedded data accumulation scheme using drones for internet of things. IEEE Wirel Commun Lett 11(5):972–976

    Google Scholar 

  • ISO (2018) Information technology security techniques information security risk management. In: Standard ISO/IEC 27005

  • Issa W, Moustafa N, Turnbull B, Sohrabi N, Tari Z (2022) Blockchain-based federated learning for securing internet of things: a comprehensive survey. ACM Comput Surv

  • Jabir RM, Khanji SIR, Ahmad LA, Alfandi O, Said H (2016) Analysis of cloud computing attacks and countermeasures. In: 2016 18th international conference on advanced communication technology (ICACT), pp 117–123. IEEE

  • Jere MS, Farnan T, Koushanfar F (2020) A taxonomy of attacks on federated learning. IEEE Secur Privacy 19(2):20–28

    Google Scholar 

  • Jiang Y, Wang S, Valls V, Ko BJ, Lee W-H, Leung KK, Tassiulas L (2019) Model pruning enables efficient federated learning on edge devices, arXiv preprint arXiv:1909.12326

  • Jiang JC, Kantarci B, Oktug S, Soyata T (2020a) Federated learning in smart city sensing: challenges and opportunities. Sensors 20(21):6230

    Google Scholar 

  • Jiang Y, Zhou Y, Wu D, Li C, Wang Y (2020b) On the detection of shilling attacks in federated collaborative filtering. In: 2020 international symposium on reliable distributed systems (SRDS), pp 185–194. IEEE

  • Ju C, Gao D, Mane R, Tan B, Liu Y, Guan C (2020) Federated transfer learning for EEG signal classification. In: 2020 42nd annual international conference of the IEEE engineering in medicine & biology society (EMBC), pp 3040–3045. IEEE

  • Kadhe S, Rajaraman N, Koyluoglu OO, Ramchandran K (2020) Fastsecagg: scalable secure aggregation for privacy-preserving federated learning, arXiv preprint arXiv:2009.11248

  • Kairouz P, McMahan HB, Avent B, Bellet A, Bennis M, Bhagoji AN, Bonawitz K, Charles Z, Cormode G, Cummings R et al (2019) Advances and open problems in federated learning, arXiv preprint arXiv:1912.04977

  • Kalapaaking AP, Khalil I, Rahman MS, Atiquzzaman M, Yi X, Almashor M (2022) Blockchain-based federated learning with secure aggregation in trusted execution environment for internet-of-things. IEEE Trans Ind Inform

  • Karimireddy SP, Kale S, Mohri M, Reddi S, Stich S, Suresh AT (2020) Scaffold: stochastic controlled averaging for federated learning. In: International conference on machine learning, PMLR, pp 5132–5143

  • Kato F, Cao Y, Yoshikawa M (2022) Olive: oblivious and differentially private federated learning on trusted execution environment, arXiv preprint arXiv:2202.07165

  • Khatri S, Vachhani H, Shah S, Bhatia J, Chaturvedi M, Tanwar S, Kumar N (2021) Machine learning models and techniques for Vanet based traffic management: implementation issues and challenges. Peer-to-Peer Netw Appl 14(3):1778–1805

    Google Scholar 

  • Khazbak Y, Tan T, Cao G (2020) Mlguard: mitigating poisoning attacks in privacy preserving distributed collaborative learning. In: 2020 29th international conference on computer communications and networks (ICCCN), pp 1–9

  • Khoa TV, Saputra YM, Hoang DT, Trung NL, Nguyen D, Ha NV, Dutkiewicz E (2020) Collaborative learning model for cyberattack detection systems in IoT industry 4.0. In: 2020 IEEE wireless communications and networking conference (WCNC), pp 1–6, IEEE

  • Khramtsova E, Hammerschmidt C, Lagraa S, State R (2020) Federated learning for cyber security: soc collaboration for malicious url detection. In: 2020 IEEE 40th international conference on distributed computing systems (ICDCS), pp 1316–1321. IEEE

  • Kim H, Park J, Bennis M, Kim S-L (2019) Blockchained on-device federated learning. IEEE Commun Lett 24(6):1279–1283

    Google Scholar 

  • Konečnỳ J, McMahan HB, Ramage HB, Richtárik P (2016) Federated optimization: Distributed machine learning for on-device intelligence, arXiv preprint arXiv:1610.02527

  • Kong L, Liu X-Y, Sheng H, Zeng P, Chen G (2019) Federated tensor mining for secure industrial internet of things. IEEE Trans Ind Inform 16(3):2144–2153

    Google Scholar 

  • Kulkarni V, Kulkarni M, Pant A (2020) Survey of personalization techniques for federated learning. In: 2020 Fourth World Conference on Smart Trends in Systems, Security and Sustainability (WorldS4), pp 794–797. IEEE

  • Kumari A, Gupta R, Tanwar S (2021) Amalgamation of blockchain and IoT for smart cities underlying 6g communication: a comprehensive review. Comput Commun 172:102–118

    Google Scholar 

  • Kwon D, Jeon J, Park S, Kim J, Cho S (2020) Multiagent ddpg-based deep learning for smart ocean federated learning IoT networks. IEEE Internet Things J 7(10):9895–9903

    Google Scholar 

  • Lalitha A, Kilinc OC, Javidi OC, Koushanfar F (2019) Peer-to-peer federated learning on graphs, arXiv preprint arXiv:1901.11173

  • Lalle Y, Fourati M, Fourati LC, Barraca JP. A hierarchical clustering federated learning-based blockchain scheme for privacy-preserving in water demand prediction, Available at SSRN 4108575

  • Lam SK, Riedl J (2004) Shilling recommender systems for fun and profit. In: Proceedings of the 13th international conference on World Wide Web, pp 393–402

  • LEAF. Leaf—light enterprise application framework, https://www.krminc.com/portfolio/leaf/

  • Li T, Sahu AK, Zaheer M, Sanjabi M, Talwalkar A, Smith V (2018) Federated optimization in heterogeneous networks, arXiv preprint arXiv:1812.06127

  • Li T, Sanjabi M, Beirami A, Smith V (2019a) Fair resource allocation in federated learning, arXiv preprint arXiv:1905.10497

  • Li Q, Wen Z, He B (2019b) Federated learning systems: vision, hype and reality for data privacy and protection

  • Li K, Zhou H, Tu Z, Wang W, Zhang H (2020a) Distributed network intrusion detection system in satellite-terrestrial integrated networks using federated learning. IEEE Access 8:214852–214865

    Google Scholar 

  • Li Y, Chen C, Liu N, Huang H, Zheng Z, Yan Q (2020b) A blockchain-based decentralized federated learning framework with committee consensus. IEEE Netw 35(1):234–241

    Google Scholar 

  • Li Z, Sharma V, Mohanty SP (2020c) Preserving data privacy via federated learning: challenges and solutions. IEEE Consumer Electron Mag 9(3):8–16

    Google Scholar 

  • Li Y, Chang T-H, Chi C-Y (2020d) Secure federated averaging algorithm with differential privacy. In: 2020 IEEE 30th international workshop on machine learning for signal processing (MLSP), pp 1–6

  • Li T, Song L, Fragouli C (2020e) Federated recommendation system via differential privacy. In: 2020 IEEE international symposium on information theory (ISIT), pp 2592–2597. IEEE

  • Li Z, Yu H, Zhou T, Luo L, Fan M, Xu Z, Sun G (2021a) Byzantine resistant secure blockchained federated learning at the edge. IEEE Netw 35(4):295–301

    Google Scholar 

  • Li J, Meng Y, Ma L, Du S, Zhu H, Pei Q, Shen S (2021b) A federated learning based privacy-preserving smart healthcare system. IEEE Trans Ind Inform

  • Li G, Wu J, Li S, Yang W, Li C (2022) Multi-tentacle federated learning over software-defined industrial internet of things against adaptive poisoning attacks. IEEE Trans Ind Inform 19(2):1260–1269

    Google Scholar 

  • Lian X, Zhang C, Zhang H, Hsieh C-J, Zhang W, Liu J (2017) Can decentralized algorithms outperform centralized algorithms? a case study for decentralized parallel stochastic gradient descent, arXiv preprint arXiv:1705.09056

  • Lin J, Du M, Liu J (2019) Free-riders in federated learning: attacks and defenses, arXiv preprint arXiv:1911.12560

  • Lin K-Y, Huang W-R (2020a) Using federated learning on malware classification. In: 2020 22nd international conference on advanced communication technology (ICACT), pp 585–589. IEEE

  • Lin G, Liang F, Pan W, Ming Z (2020b) Fedrec: federated recommendation with explicit feedback. IEEE Intell Syst 36(5):21–30

    Google Scholar 

  • Liu S, Tang J, Wang C, Wang Q, Gaudiot J-L (2017) Implementing a cloud platform for autonomous driving, arXiv preprint arXiv:1704.02696

  • Liu K, Dolan-Gavitt B, Garg S (2018) Fine-pruning: Defending against backdooring attacks on deep neural networks. In: International symposium on research in attacks, intrusions, and defenses, pp 273–294. Springer

  • Liu Y, Ai Z, Sun S, Zhang S, Liu Z, Yu H (2020a) Fedcoin: a peer-to-peer payment system for federated learning. In: Federated learning. Springer, pp 125–138

  • Liu Y, James J, Kang J, Niyato D, Zhang S (2020b) Privacy-preserving traffic flow prediction: a federated learning approach. IEEE Internet Things J 7(8):7751–7763

    Google Scholar 

  • Liu Y, Peng J, Kang J, Iliyasu AM, Niyato D, Abd El-Latif AA (2020c) A secure federated learning framework for 5g networks. IEEE Wirel Commun 27(4):24–31

    Google Scholar 

  • Liu Y, Yuan X, Xiong Z, Kang J, Wang X, Niyato D (2020d) Federated learning for 6g communications: challenges, methods, and future directions. China Commun 17(9):105–118

    Google Scholar 

  • Liu J, He X, Sun R, Du X, Guizani M (2021) Privacy-preserving data sharing scheme with fl via mpc in financial permissioned blockchain. In: ICC 2021-IEEE international conference on communications, pp 1–6. IEEE

  • Lu Y, Huang X, Dai Y, Maharjan S, Zhang Y (2019a) Blockchain and federated learning for privacy-preserved data sharing in industrial iot. IEEE Trans Ind Inform 16(6):4177–4186

    Google Scholar 

  • Lu Y, Huang X, Dai Y, Maharjan S, Zhang Y (2019b) Differentially private asynchronous federated learning for mobile edge computing in urban informatics. IEEE Trans Ind Inform 16(3):2134–2143

    Google Scholar 

  • Lu X, Liao Y, Lio P, Hui P (2020a) Privacy-preserving asynchronous federated learning mechanism for edge network computing. IEEE Access 8:48970–48981

    Google Scholar 

  • Lu Y, Huang X, Zhang K, Maharjan S, Zhang Y (2020b) Blockchain empowered asynchronous federated learning for secure data sharing in internet of vehicles. IEEE Trans Veh Technol 69(4):4298–4311

    Google Scholar 

  • Lu Y, Huang X, Dai Y, Maharjan S, Zhang Y (2020c) Federated learning for data privacy preservation in vehicular cyber-physical systems. IEEE Netw 34(3):50–56

    Google Scholar 

  • Lu Y, Huang X, Zhang K, Maharjan S, Zhang Y (2020d) Blockchain empowered asynchronous federated learning for secure data sharing in internet of vehicles. IEEE Trans Veh Technol 69(4):4298–4311

    Google Scholar 

  • Lu Y, Huang X, Zhang K, Maharjan S, Zhang Y (2020e) Blockchain empowered asynchronous federated learning for secure data sharing in internet of vehicles. IEEE Trans Veh Technol 69(4):4298–4311

    Google Scholar 

  • Lyu L, Yu J, Nandakumar K, Li Y, Ma X, Jin J, Yu H, Ng KS (2020a) Towards fair and privacy-preserving federated deep models. IEEE Trans Parallel Distrib Syst 31(11):2524–2541

    Google Scholar 

  • Lyu L, Yu H, Ma X, Sun L, Zhao J, Yang Q, Yu PS (2020b) Privacy and robustness in federated learning: attacks and defenses, arXiv preprint arXiv:2012.06337

  • Ma C, Li J, Ding M, Yang HH, Shu F, Quek TQ, Poor HV (2020a) On safeguarding privacy and security in the framework of federated learning. IEEE Netw 34(4):242–248

    Google Scholar 

  • Ma C, Li J, Ding M, Yang HH, Shu F, Quek TQ, Poor HV (2020b) On safeguarding privacy and security in the framework of federated learning. IEEE Netw 34(4):242–248

    Google Scholar 

  • Ma B, Wu J, Liu W, Chiaraviglio L, Ming X (2020c) Combating hard or soft disasters with privacy-preserving federated mobile buses-and-drones based networks. In: 2020 IEEE 21st international conference on information reuse and integration for data science (IRI), pp 31–36. IEEE

  • Ma Z, Ma J, Miao Y, Liu X, Choo K-KR, Deng R (2021) Pocket diagnosis: secure federated learning against poisoning attack in the cloud. IEEE Trans Serv Comput

  • Madi A, Stan O, Mayoue A, Grivet-Sébert A, Gouy-Pailler C, Sirdey R (2021) A secure federated learning framework using homomorphic encryption and verifiable computing. In: 2021 reconciling data analytics, automation, privacy, and security: a big data challenge (RDAAPS), pp 1–8

  • Mahjabin T, Xiao Y, Sun G, Jiang W (2017) A survey of distributed denial-of-service attack, prevention, and mitigation techniques. Int J Distrib Sens Netw 13(12):1550147717741463

    Google Scholar 

  • Majeed U, Hassan SS, Hong CS (2021) Cross-silo model-based secure federated transfer learning for flow-based traffic classification. In: 2021 international conference on information networking (ICOIN), pp 588–593. IEEE

  • Maniak T, Iqbal R, Doctor F (2018) Traffic modelling, visualisation and prediction for urban mobility management. In: Advances in hybridization of intelligent methods. Springer, pp 57–70

  • Manias DM, Shami A (2021) Making a case for federated learning in the internet of vehicles and intelligent transportation systems. IEEE Netw 35(3):88–94

    Google Scholar 

  • Mansour Y, Mohri M, Ro J, Suresh AT (2020) Three approaches for personalization with applications to federated learning, arXiv preprint arXiv:2002.10619

  • Mao J, Cao C, Wang L, Ye J, Zhong W (2021) Research on the security technology of federated learning privacy preserving. J Phys 1757:012192

    Google Scholar 

  • Marfoq O, Xu C, Neglia G, Vidal R (2020) Throughput-optimal topology design for cross-silo federated learning, arXiv preprint arXiv:2010.12229

  • McMahan B, Moore E, Ramage D, Hampson S, Arcas BAY (2017) Communication-efficient learning of deep networks from decentralized data. In: Artificial intelligence and statistics, PMLR, pp 1273–1282

  • Meng D, Li H, Zhu F, Li X (2020) Fedmonn: meta operation neural network for secure federated aggregation. In: 2020 IEEE 22nd international conference on high performance computing and communications; IEEE 18th international conference on smart city; IEEE 6th international conference on data science and systems (HPCC/SmartCity/DSS), pp 579–584. IEEE

  • Mo F, Haddadi H (2019) Efficient and private federated learning using tee In: Proc. EuroSys Conf

  • Mo F, Haddadi H, Katevas K, Marin E, Perino D, Kourtellis N (2021) Ppfl: privacy-preserving federated learning with trusted execution environments, arXiv preprint arXiv:2104.14380

  • Mothukuri V, Parizi RM, Pouriyeh S, Huang Y, Dehghantanha A, Srivastava G (2021) A survey on security and privacy of federated learning. Future Gener Comput Syst 115:619–640

    Google Scholar 

  • Moubayed A, Sharif M, Luccini M, Primak S, Shami A (2021) Water leak detection survey: challenges & research opportunities using data fusion & federated learning. IEEE Access 9:40595–40611

    Google Scholar 

  • Moulahi T, Jabbar R, Alabdulatif A, Abbas S, El Khediri S, Zidi S, Rizwan M (2022) Privacy-preserving federated learning cyber-threat detection for intelligent transport systems with blockchain-based security. Expert Syst e13103

  • Moustafa N, Keshk N, Debie N, Janicke H (2020) Federated ton_iot windows datasets for evaluating ai-based security applications. In: 2020 IEEE 19th international conference on trust, security and privacy in computing and communications (TrustCom), pp 848–855. IEEE

  • Mowla NI, Tran NH, Doh I, Chae K (2019) Federated learning-based cognitive detection of jamming attack in flying ad-hoc network. IEEE Access 8:4338–4350

    Google Scholar 

  • Nasr M, Shokri R, Houmansadr A (2019) Comprehensive privacy analysis of deep learning: passive and active white-box inference attacks against centralized and federated learning. In: 2019 IEEE symposium on security and privacy (SP), pp 739–753. IEEE

  • Nguyen TD, Marchal S, Miettinen M, Fereidooni H, Asokan N, Sadeghi A-R (2019) Dïot: a federated self-learning anomaly detection system for iot. In: 2019 IEEE 39th international conference on distributed computing systems (ICDCS), pp 756–767. IEEE

  • Nguyen DC, Ding M, Pathirana PN, Seneviratne A, Li J, Niyato D, Poor HV (2021a) Federated learning for industrial internet of things in future industries, arXiv preprint arXiv:2105.14659

  • Nguyen DC, Ding M, Pathirana PN, Seneviratne A, Li J, Poor HV (2021b) Federated learning for internet of things: a comprehensive survey, arXiv preprint arXiv:2104.07914

  • Nguyen TD, Rieger P, Yalame H, Möllering H, Fereidooni H, Marchal S, Miettinen M, Mirhoseini A, Sadeghi A-R, Schneider T et al (2021c) Flguard: secure and private federated learning, arXiv preprint arXiv:2101.02281

  • Nilsson A, Smith S, Ulm G, Gustavsson E, Jirstrand M (2018) A performance evaluation of federated learning algorithms. In: Proceedings of the second workshop on distributed infrastructures for deep learning, pp 1–8

  • Nuding F, Mayer R (2020) Poisoning attacks in federated learning: an evaluation on traffic sign classification. In: Proceedings of the tenth ACM conference on data and application security and privacy, pp 168–170

  • Openmined, Let’s solve privacy. https://www.openmined.org/

  • Otoum S, Ridhawi I Al, Mouftah H (2021) Securing critical iot infrastructures with blockchain-supported federated learning. IEEE Internet Things J

  • PaddlePaddle. Baidu paddlepaddle releases 21 new capabilities to accelerate industry-grade model development. http://research.baidu.com/Blog/index-view?id=126

  • Pan Q, Wu J, Bashir AK, Li J, Yang W, Al-Otaibi YD (2021) Joint protection of energy security and information privacy for energy harvesting: an incentive federated learning approach. IEEE Trans Ind Inform

  • Papernot N, Abadi M, Erlingsson U, Goodfellow I, Talwar K (2016) Semi-supervised knowledge transfer for deep learning from private training data, arXiv preprint arXiv:1610.05755

  • Parekh R, Patel N, Gupta R, Jadav NK, Tanwar S, Alharbi A, Tolba A, Neagu B-C, Raboaca MS (2023) Gefl: gradient encryption-aided privacy preserved federated learning for autonomous vehicles. IEEE Access 11:1825–1839

    Google Scholar 

  • Park S, Jung S, Lee H, Kim J, Kim J-H (2021) Large-scale water quality prediction using federated sensing and learning: a case study with real-world sensing big-data. Sensors 21(4):1462

    Google Scholar 

  • Passerat-Palmbach J, Farnan T, McCoy M, Harris JD, Manion ST, Flannery HL, Gleim B (2020) Blockchain-orchestrated machine learning for privacy preserving federated learning in electronic health data. In: 2020 IEEE international conference on blockchain (Blockchain), pp 550–555. IEEE

  • Patel VA, Bhattacharya P, Tanwar S, Jadav NK, Gupta R (2022a) Bfledge: blockchain based federated edge learning scheme in v2x underlying 6g communications. In: 2022 12th international conference on cloud computing, data science & engineering (Confluence), pp 146–152

  • Patel VA, Bhattacharya P, Tanwar S, Gupta R, Sharma G, Bokoro PN, Sharma R (2022b) Adoption of federated learning for healthcare informatics: emerging applications and future directions. IEEE Access 10:90792–90826

    Google Scholar 

  • Paul S, Sengupta P, Mishra S (2020) Flaps: federated learning and privately scaling. In: 2020 IEEE 17th international conference on mobile ad hoc and sensor systems (MASS), pp 13–19. IEEE

  • Popoola SI, Ande R, Adebisi B, Gui G, Hammoudeh M, Jogunola O (2021) Federated deep learning for zero-day botnet attack detection in IoT edge devices. IEEE Internet Things J 9(5):3930–3944

    Google Scholar 

  • Qin Y, Kondo M (2021) Mlmg: multi-local and multi-global model aggregation for federated learning. In: 2021 IEEE international conference on pervasive computing and communications workshops and other affiliated events (PerCom Workshops), pp 565–571. IEEE

  • Qin Y, Matsutani H, Kondo M (2020) A selective model aggregation approach in federated learning for online anomaly detection. In: 2020 International Conferences on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics (Cybermatics), pp 684–691. IEEE

  • Qu Y, Gao L, Luan TH, Xiang Y, Yu S, Li B, Zheng G (2020) Decentralized privacy using blockchain-enabled federated learning in fog computing. IEEE Internet Things J 7(6):5171–5183

    Google Scholar 

  • Rahman SA, Tout H, Ould-Slimane H, Mourad A, Talhi C, Guizani M (2020a) A survey on federated learning: the journey from centralized to distributed on-site learning and beyond. IEEE Internet Things J 8(7):5476–5497

    Google Scholar 

  • Rahman MA, Hossain MS, Islam MS, Alrajeh NA, Muhammad G (2020) Secure and provenance enhanced internet of health things framework: a blockchain managed federated learning approach. IEEE Access 8:205071–205087

    Google Scholar 

  • Ramaswamy S, Mathews R, Rao K, Beaufays F (2019) Federated learning for emoji prediction in a mobile keyboard, arXiv preprint arXiv:1906.04329

  • Rieke N, Hancox J, Li W, Milletari F, Roth HR, Albarqouni S, Bakas S, Galtier MN, Landman BA, Maier-Hein K et al (2020) The future of digital health with federated learning. NPJ Digital Med 3(1):1–7

    Google Scholar 

  • Sabt M, Achemlal M, Bouabdallah A (2015) Trusted execution environment: what it is, and what it is not. In: 2015 IEEE Trustcom/BigDataSE/ISPA, vol 1, pp 57–64. IEEE

  • Saha S, Ahmad T (2020) Federated transfer learning: concept and applications, arXiv preprint arXiv:2010.15561

  • Samarakoon S, Bennis M, Saad W, Debbah M (2018) Federated learning for ultra-reliable low-latency v2v communications. In: 2018 IEEE global communications conference (GLOBECOM), pp 1–7. IEEE

  • Saraswat D, Verma A, Bhattacharya P, Tanwar S, Sharma G, Bokoro PN, Sharma R (2022) Blockchain-based federated learning in UAVs beyond 5g networks: a solution taxonomy and future directions. IEEE Access 10:33154–33182

    Google Scholar 

  • Sater RA, Hamza AB (2020) A federated learning approach to anomaly detection in smart buildings, arXiv preprint arXiv:2010.10293

  • Sattler F, Müller K-R, Samek W (2020) Clustered federated learning: model-agnostic distributed multitask optimization under privacy constraints. IEEE Trans Neural Netw Learn Syst 32(8):3710–3722

    MathSciNet  Google Scholar 

  • Sav S, Pyrgelis A, Troncoso-Pastoriza JR, Froelicher D, Bossuat J-P, Sousa JS, Hubaux J-P (2020) Poseidon: privacy-preserving federated neural network learning, arXiv preprint arXiv:2009.00349

  • Schneble W, Thamilarasu G (2019) Attack detection using federated learning in medical cyber-physical systems. In: 2019 28th international conference on computer communication and networks, ICCCN, pp 1–8

  • Seo H, Park J, Oh S, Bennis M, Kim S-L (2020) Federated knowledge distillation, arXiv preprint arXiv:2011.02367

  • Shafee A, Baza M, Talbert DA, Fouda MM, Nabil M, Mahmoud M (2020) Mimic learning to generate a shareable network intrusion detection model. In: 2020 IEEE 17th annual consumer communications & networking conference (CCNC), pp 1–6. IEEE

  • Shah U, Dave I, Malde J, Mehta J, Kodeboyina S (2021) Maintaining privacy in medical imaging with federated learning, deep learning, differential privacy, and encrypted computation. In: 2021 6th international conference for convergence in technology (I2CT), pp 1–6. IEEE

  • Shayan M, Fung C, Yoon CJM, Beschastnikh I (2021) Biscotti: a blockchain system for private and secure federated learning. IEEE Trans Parallel Distrib Syst 32(7):1513–1525

    Google Scholar 

  • Shejwalkar V, Houmansadr A (2021) Manipulating the byzantine: optimizing model poisoning attacks and defenses for federated learning. Internet Society, p 18

  • Silva S, Gutman BA, Romero E, Thompson PM, Altmann A, Lorenzi M (2019) Federated learning in distributed medical databases: meta-analysis of large-scale subcortical brain data. In: 2019 IEEE 16th international symposium on biomedical imaging (ISBI 2019), pp 270–274. IEEE

  • Singh AK, Blanco-Justicia A, Domingo-Ferrer J, Sánchez D, Rebollo-Monedero D (2020) Fair detection of poisoning attacks in federated learning. In: 2020 IEEE 32nd international conference on tools with artificial intelligence (ICTAI), pp 224–229. IEEE

  • Sirohi D, Kumar N, Rana PS (2020) Convolutional neural networks for 5g-enabled intelligent transportation system: a systematic review. Comput Commun 153:459–498

    Google Scholar 

  • So J, Güler B, Avestimehr AS (2021) Turbo-aggregate: breaking the quadratic aggregation barrier in secure federated learning. IEEE J Sel Areas Inf Theory 2(1):479–489

    Google Scholar 

  • Song M, Wang Z, Zhang Z, Song Y, Wang Q, Ren J, Qi H (2020a) Analyzing user-level privacy attack against federated learning. IEEE J Sel Areas Commun 38(10):2430–2444

    Google Scholar 

  • Song Y, Liu T, Wei T, Wang X, Tao Z, Chen M (2020) Fda3: federated defense against adversarial attacks for cloud-based IIoT applications. IEEE Trans Ind Inform 17(11):7830–7838

    Google Scholar 

  • Suarez-Tangil G, Dash SK, Ahmadi M, Kinder J, Giacinto G, Cavallaro G (2017) Droidsieve: fast and accurate classification of obfuscated android malware. In: Proceedings of the seventh ACM on conference on data and application security and privacy, pp 309–320

  • Sun L, Lyu L (2020) Federated model distillation with noise-free differential privacy, arXiv preprint arXiv:2009.05537

  • Sun F, Zang W, Gravina R, Fortino G, Li Y (2020a) Gait-based identification for elderly users in wearable healthcare systems. Inf Fusion 53:134–144

    Google Scholar 

  • Sun Y, Ochiai H, Esaki H (2020b) Intrusion detection with segmented federated learning for large-scale multiple lans. In: 2020 international joint conference on neural networks (IJCNN), pp 1–8. IEEE

  • Suri N (2019) Distributed systems security knowledge area issue. The Cyber Security Body Of Knowledge

  • Tabassum A, Erbad A, Lebda W, Mohamed A, Guizani M (2022) Fedgan-ids: privacy-preserving ids using gan and federated learning. Comput Commun 192:299–310

    Google Scholar 

  • Taheri R, Shojafar M, Alazab M, Tafazolli R (2020) Fed-IIoT: a robust federated malware detection architecture in industrial IoT. IEEE Trans Ind Inform 17(12):8442–8452

    Google Scholar 

  • Tan AZ, Yu H, Cui L, Yang Q (2022) Towards personalized federated learning. IEEE Trans Neural Netw Learning Syst

  • Tao Z, Li Q (2018) esgd: communication efficient distributed deep learning on the edge. In: USENIX Workshop on Hot Topics in Edge Computing (HotEdge 18)

  • Tensor/IO. Tensor/io, https://doc-ai.github.io/tensorio/

  • Triastcyn A, Faltings B (2019) Federated learning with bayesian differential privacy. In: 2019 IEEE international conference on Big Data (Big Data), pp 2587–2596. IEEE

  • Truex S, Baracaldo N, Anwar A, Steinke T, Ludwig H, Zhang R, Zhou Y (2019) A hybrid approach to privacy-preserving federated learning. In: Proceedings of the 12th ACM workshop on artificial intelligence and security, pp 1–11

  • Truong N, Sun K, Wang S, Guitton F, Guo Y (2020) Privacy preservation in federated learning: an insightful survey from the gdpr perspective, arXiv preprint arXiv:2011.05411

  • Uprety A, Rawat DB, Li J (2021) Privacy preserving misbehavior detection in iov using federated machine learning. In: 2021 IEEE 18th annual consumer communications & networking conference (CCNC), pp 1–6. IEEE

  • Vanhaesebrouck P, Bellet A, Tommasi M (2017) Decentralized collaborative learning of personalized models over networks. In: Artificial Intelligence and Statistics. PMLR, pp 509–517

  • Verma A, Bhattacharya P, Bodkhe U, Saraswat D, Tanwar S, Dev K (2022) Fedrec: trusted rank-based recommender scheme for service provisioning in federated cloud environment. Digital Commun Netw

  • Vimalajeewa D, Kulatunga C, Berry D, Balasubramaniam S (2021) A service-based joint model used for distributed learning: application for smart agriculture. IEEE Trans Emerg Topics Comput 10(2):838–854

    Google Scholar 

  • Wainakh A, Guinea AS, Grube T, Mühlhäuser M (2020) Enhancing privacy via hierarchical federated learning. In: 2020 IEEE European symposium on security and privacy workshops (EuroS &PW), pp 344–347. IEEE

  • Wang S, Qiao Z (2019) Robust pervasive detection for adversarial samples of artificial intelligence in IoT environments. IEEE Access 7:88693–88704

    Google Scholar 

  • Wang Z, Song M, Zhang Z, Song Y, Wang Q, Qi H (2019) Beyond inferring class representatives: user-level privacy leakage from federated learning. In: IEEE INFOCOM 2019-IEEE conference on computer communications, pp 2512–2520. IEEE

  • Wang Y, Su Z, Zhang N, Benslimane A (2020a) Learning in the air: secure federated learning for UAV-assisted crowdsensing. IEEE Trans Netw Sci Eng 8(2):1055–1069

    Google Scholar 

  • Wang H, Sreenivasan K, Rajput S, Vishwakarma H, Agarwal S, Sohn J-Y, Lee K, Papailiopoulos D (2020b) Attack of the tails: yes, you really can backdoor federated learning, arXiv preprint arXiv:2007.05084

  • Wang H, Yurochkin M, Sun Y, Papailiopoulos D, Khazaeni Y (2020c) Federated learning with matched averaging, arXiv preprint arXiv:2002.06440

  • Wang X, Garg S, Lin H, Hu J, Kaddoum G, Piran MJ, Hossain MS (2021) Towards accurate anomaly detection in industrial internet-of-things using hierarchical federated learning. IEEE Internet Things J 9(10):7110–7119

    Google Scholar 

  • Wazzeh M, Ould-Slimane H, Talhi C, Mourad A, Guizani M (2022) Privacy-preserving continuous authentication for mobile and iot systems using warmup-based federated learning. IEEE Netw

  • Wei J, Zhu Q, Li Q, Nie L, Shen Z, Choo K-K R, Yu K (2022) A redactable blockchain framework for secure federated learning in industrial internet-of-things. IEEE Internet Things J

  • Wu D, Pan M, Xu Z, Zhang Y, Han Z (2020) Towards efficient secure aggregation for model update in federated learning. In: GLOBECOM 2020–2020 IEEE global communications conference, pp 1–6

  • Wu M, Ye D, Ding J, Guo Y, Yu R, Pan M (2021) Incentivizing differentially private federated learning: a multidimensional contract approach. IEEE Internet Things J 8(13):10639–10651

    Google Scholar 

  • Xia Q, Gao X, Xu Z (2014) Double auctions for federated learning in satellite edge clouds. Available at SSRN 4220613

  • Xie C, Huang K, Chen P-Y, Li B (2019) Dba: distributed backdoor attacks against federated learning. In: International Conference on Learning Representations

  • Xing J, Jiang Z, Yin H (2020) Jupiter: a modern federated learning platform for regional medical care. In: 2020 ieee international conference on joint cloud computing, pp 21–21. IEEE

  • Xin B, Yang W, Geng Y, Chen S, Wang S, Huang L (2020) Private fl-gan: differential privacy synthetic data generation based on federated learning. In: ICASSP 2020–2020 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 2927–2931, IEEE

  • Xu G, Li H, Liu S, Yang K, Lin X (2019a) Verifynet: secure and verifiable federated learning. IEEE Trans Inf Forensics Secur 15:911–926

    Google Scholar 

  • Xu R, Baracaldo N, Zhou Y, Anwar A, Ludwig H (2019b) Hybridalpha: an efficient approach for privacy-preserving federated learning. In: Proceedings of the 12th ACM workshop on artificial intelligence and security, pp 13–23

  • Xu G, Li H, Zhang Y, Xu S, Ning J, Deng R (2020) Privacy-preserving federated deep learning with irregular users. IEEE Trans Dependable Secure Comput 19(2):1364–1381

    Google Scholar 

  • Yang T, Andrew G, Eichner H, Sun H, Li W, Kong N, Ramage D, Beaufays F (2018) Applied federated learning: Improving google keyboard query suggestions, arXiv preprint arXiv:1812.02903

  • Yang Q, Liu Y, Chen T, Tong Y (2019) Federated machine learning: concept and applications. ACM Trans Intell Syst Technol 10(2):1–19

    Google Scholar 

  • Yang H, He H, Zhang W, Cao X (2020) Fedsteg: a federated transfer learning framework for secure image steganalysis. IEEE Trans Netw Sci Eng 8(2):1084–1094

    Google Scholar 

  • Yao J, Ansari N (2021) Secure federated learning by power control for internet of drones. IEEE Trans Cognitive Commun Netw 7(4):1021–1031

    Google Scholar 

  • Yu T, Li T, Sun Y, Nanda S, Smith V, Sekar V, Seshan S (2020a) Learning context-aware policies from multiple smart homes via federated multi-task learning. In: 2020 IEEE/ACM fifth international conference on internet-of-things design and implementation (IoTDI), pp 104–115. IEEE

  • Yu F, Zhang W, Qin Z, Xu Z, Wang D, Liu C, Tian Z, Chen X (2020b) Heterogeneous federated learning, arXiv preprint arXiv:2008.06767

  • Yuan X, Chen J, Zhang N, Fang X, Liu D (2021) A federated bidirectional connection broad learning scheme for secure data sharing in internet of vehicles. China Commun 18(7):117–133

    Google Scholar 

  • Zhan Y, Zhang J, Hong Z, Wu L, Li P, Guo S (2021) A survey of incentive mechanism design for federated learning. IEEE Trans Emerg Topics Comput 10(2):1035–1044

    Google Scholar 

  • Zhang J, Chen J, Wu D, Chen B, Yu S (2019) Poisoning attack in federated learning using generative adversarial nets. In: 2019 18th IEEE international conference on trust, security and privacy in computing and communications/13th IEEE international conference on big data science and engineering (TrustCom/BigDataSE), pp 374–380. IEEE

  • Zhang X, Fang F, Wang J (2020a) Probabilistic solar irradiation forecasting based on variational Bayesian inference with secure federated learning. IEEE Trans Ind Inform 17(11):7849–7859

    Google Scholar 

  • Zhang X, Chen X, Liu JK, Xiang Y (2020b) Deeppar and deepdpa: privacy preserving and asynchronous deep learning for industrial iot. IEEE Trans Ind Inf 16(3):2081–2090

    Google Scholar 

  • Zhang J, Chen B, Cheng X, Binh HTT, Yu S (2020c) Poisongan: generative poisoning attacks against federated learning in edge computing systems. IEEE Internet Things J 8(5):3310–3322

    Google Scholar 

  • Zhang C, Li S, Xia J, Wang W, Yan F, Liu Y (2020d) Batchcrypt: efficient homomorphic encryption for cross-silo federated learning. In: 2020 USENIX annual technical conference (USENIXATC 20), pp 493–506

  • Zhang Y, Wu Q, Shikh-Bahaei M (2020e) Vertical federated learning based privacy-preserving cooperative sensing in cognitive radio networks. In: 2020 IEEE globecom workshops (GC Wkshps), pp 1–6. IEEE

  • Zhang Y, Wang Z, Cao J, Hou R, Meng D (2021) Shufflefl: gradient-preserving federated learning using trusted execution environment. In: Proceedings of the 18th ACM international conference on computing frontiers, pp 161–168

  • Zhang Z, Wu L, He D, Wang Q, Wu D, Shi X, Ma C (2022) G-vcfl: grouped verifiable chained privacy-preserving federated learning. IEEE Trans Netw Serv Manag

  • Zhao K, Xi W, Wang Z, Zhao J, Wang R, Jiang Z (2020a) Smss: secure member selection strategy in federated learning. IEEE Intell Syst 35(4):37–49

    Google Scholar 

  • Zhao Y, Zhao J, Yang M, Wang T, Wang N, Lyu L, Niyato D, Lam K-Y (2020b) Local differential privacy-based federated learning for internet of things. IEEE Internet Things J 8(11):8836–8853

    Google Scholar 

  • Zhao Y, Zhao J, Jiang L, Tan R, Niyato D, Li Z, Lyu L, Liu Y (2020c) Privacy-preserving blockchain-based federated learning for IoT devices. IEEE Internet Things J 8(3):1817–1829

    Google Scholar 

  • Zhao Y, Zhao J, Jiang L, Tan R, Niyato D, Li Z, Lyu L, Liu Y (2020d) Privacy-preserving blockchain-based federated learning for IoT devices. IEEE Internet Things J 8(3):1817–1829

    Google Scholar 

  • Zhao S, Bharati R, Borcea C, Chen Y (2020e) Privacy-aware federated learning for page recommendation. In: 2020 IEEE international conference on Big Data (Big Data), pp 1071–1080. IEEE

  • Zhao L, Tang X, You Z, Pang Y, Xue H, Zhu L (2020f) Operation and security considerations of federated learning platform based on compute first network. In: 2020 IEEE/CIC international conference on communications in China (ICCC Workshops), pp 117–121. IEEE

  • Zhao L, Tang X, You Z, Pang Y, Xue H, Zhu L (2020g) Operation and security considerations of federated learning platform based on compute first network. In: 2020 IEEE/CIC international conference on communications in China (ICCC Workshops), pp 117–121. IEEE

  • Zhao L, Jiang J, Feng B, Wang Q, Shen C, Li Q (2021a) Sear: secure and efficient aggregation for byzantine-robust federated learning. IEEE Trans Dependable Secur Comput 19(5):3329–3342

    Google Scholar 

  • Zhao B, Fan K, Yang K, Wang Z, Li H, Yang Y (2021b) Anonymous and privacy-preserving federated learning with industrial big data. IEEE Trans Ind Inform 17(9):6314–6323

    Google Scholar 

  • Zheng H, Hu H, Han Z (2020) Preserving user privacy for machine learning: local differential privacy or federated machine learning? IEEE Intell Syst 35(4):5–14

    Google Scholar 

  • Zhou P, Wang K, Guo L, Gong S, Zheng B (2019) A privacy-preserving distributed contextual federated online learning framework with big data support in social recommender systems. IEEE Trans Knowl Data Eng 33(3):824–838

    Google Scholar 

  • Zhou Z, Yang S, Pu L, Yu S (2020) Cefl: online admission control, data scheduling, and accuracy tuning for cost-efficient federated learning across edge nodes. IEEE Internet Things J 7(10):9341–9356

    Google Scholar 

  • Zhou Z, Tian Y, Peng C, Yang N, Long S (2022) Vflf: a verifiable federated learning framework against malicious aggregators in industrial internet of things. Concurr Comput e7193

  • Zhu H, Goh RSM, Ng W-K (2020) Privacy-preserving weighted federated learning within the secret sharing framework. IEEE Access 8:198275–198284

    Google Scholar 

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Sirohi, D., Kumar, N., Rana, P.S. et al. Federated learning for 6G-enabled secure communication systems: a comprehensive survey. Artif Intell Rev 56, 11297–11389 (2023). https://doi.org/10.1007/s10462-023-10417-3

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