survey

Blockchain-empowered Federated Learning: Challenges, Solutions, and Future Directions

Authors:

Houda FerradiAuthors Info & Claims

ACM Computing Surveys, Volume 55, Issue 11

Article No.: 240, Pages 1 - 31

https://doi.org/10.1145/3570953

Published: 22 February 2023 Publication History

Abstract

Federated learning is a privacy-preserving machine learning technique that trains models across multiple devices holding local data samples without exchanging them. There are many challenging issues in federated learning, such as coordinating participants’ activities, arbitrating their benefits, and aggregating models. Most existing solutions employ a centralized approach, in which a trustworthy central authority is needed for coordination. Such an approach incurs many disadvantages, including vulnerability to attacks, lack of credibility, and difficulty in calculating rewards. Recently, blockchain was identified as a potential solution for addressing the abovementioned issues. Extensive research has been conducted, and many approaches, methods, and techniques have been proposed. There is a need for a systematic survey to examine how blockchain can empower federated learning. Although there are many surveys on federated learning, few of them cover blockchain as an enabling technology. This work comprehensively surveys challenges, solutions, and future directions for blockchain-empowered federated learning (BlockFed). First, we identify the critical issues in federated learning and explain why blockchain provides a potential approach to addressing these issues. Second, we categorize existing system models into three classes: decoupled, coupled, and overlapped, according to how the federated learning and blockchain functions are integrated. Then we compare the advantages and disadvantages of these three system models, regard the disadvantages as challenging issues in BlockFed, and investigate corresponding solutions. Finally, we identify and discuss the future directions, including open problems in BlockFed.

References

[1]

Martin Abadi, Andy Chu, Ian Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang. 2016. Deep learning with differential privacy. In ACM SIGSAC Conference on Computer and Communications Security (CCS). ACM, Vienna, Austria, 308–318.

Digital Library

[2]

Mansoor Ali, Hadis Karimipour, and Muhammad Tariq. 2021. Integration of blockchain and federated learning for internet of things: Recent advances and future challenges. Computers & Security 108 (2021), 102355.

Digital Library

[3]

Moayad Aloqaily, Ismaeel Al Ridhawi, and Mohsen Guizani. 2022. Energy-aware blockchain and federated learning-supported vehicular networks. IEEE Trans. Intell. Transp. Syst. 23, 11 (2022), 22641–22652. DOI:

[4]

Saeed Hamood Alsamhi, Faris A. Almalki, Fatemeh Afghah, Ammar Hawbani, Alexey V. Shvetsov, Brian Lee, and Houbing Song. 2021. Drones’ edge intelligence over smart environments in B5G: Blockchain and federated learning synergy. IEEE Transactions on Green Communications and Networking (TGCN) 6, 1 (2021), 295–312.

[5]

Manoj Ghuhan Arivazhagan, Vinay Aggarwal, Aaditya Kumar Singh, and Sunav Choudhary. 2019. Federated learning with personalization layers. CoRR abs/1912.00818 (2019), 1–13. http://arxiv.org/abs/1912.00818.

[6]

Sana Awan, Fengjun Li, Bo Luo, and Mei Liu. 2019. Poster: A reliable and accountable privacy-preserving federated learning framework using the blockchain. In ACM SIGSAC Conference on Computer and Communications Security (CCS). ACM, London, UK, 2561–2563.

Digital Library

[7]

Paolo Bellavista, Luca Foschini, and Alessio Mora. 2021. Decentralised learning in federated deployment environments: A system-level survey. ACM Computing Surveys (CSUR) 54, 1 (2021), 1–38.

Digital Library

[8]

Peva Blanchard, El Mahdi El Mhamdi, Rachid Guerraoui, and Julien Stainer. 2017. Machine learning with adversaries: Byzantine tolerant gradient descent. In Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA, Isabelle Guyon, Ulrike von Luxburg, Samy Bengio, Hanna M. Wallach, Rob Fergus, S. V. N. Vishwanathan, and Roman Garnett (Eds.). 119–129. https://proceedings.neurips.cc/paper/2017/hash/f4b9ec30ad9f68f89b29639786cb62ef-Abstract.html.

[9]

Keith Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloe Kiddon, Jakub Konečnỳ, Stefano Mazzocchi, H. Brendan McMahan, et al. 2019. Towards federated learning at scale: System design. In Second Conference on Machine Learning and Systems (MLSys). mlsys.org, Stanford, CA, 1–15.

[10]

Keith Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H. Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, and Karn Seth. 2017. Practical secure aggregation for privacy-preserving machine learning. In ACM SIGSAC Conference on Computer and Communications Security (CCS). ACM, Dallas, TX, USA, 1175–1191.

Digital Library

[11]

Ran Canetti, Uri Feige, Oded Goldreich, and Moni Naor. 1996. Adaptively secure multi-party computation. In Twenty-eighth Annual ACM Symposium on Theory of Computing (STOC). ACM, New York, USA, 639–648.

[12]

Mingrui Cao, Long Zhang, and Bin Cao. 2021. Towards on-device federated learning: A direct acyclic graph-based blockchain approach. CoRR abs/2104.13092 (2021). arXiv:2104.13092 https://arxiv.org/abs/2104.13092.

[13]

Haoye Chai, Supeng Leng, Yijin Chen, and Ke Zhang. 2020. A hierarchical blockchain-enabled federated learning algorithm for knowledge sharing in internet of vehicles. IEEE Transactions on Intelligent Transportation Systems (T-ITS) 22, 7 (2020), 3975–3986.

Digital Library

[14]

Lingjiao Chen, Hongyi Wang, Zachary Charles, and Dimitris Papailiopoulos. 2018. DRACO: Byzantine-resilient distributed training via redundant gradients. In International Conference on Machine Learning (ICML), Vol. 80. PMLR, 902–911.

[15]

Yudong Chen, Lili Su, and Jiaming Xu. 2017. Distributed statistical machine learning in adversarial settings: Byzantine gradient descent. Proceedings of the ACM on Measurement and Analysis of Computing Systems (POMACS) 1, 2 (2017), 1–25.

Digital Library

[16]

Luca Corinzia and Joachim M. Buhmann. 2019. Variational federated multi-task learning. CoRR abs/1906.06268 (2019), 1–12.

[17]

Laizhong Cui, Xiaoxin Su, Zhongxing Ming, Ziteng Chen, Shu Yang, Yipeng Zhou, and Wei Xiao. 2022. CREAT: Blockchain-assisted compression algorithm of federated learning for content caching in edge computing. IEEE Internet Things J. 9, 16 (2022), 14151–14161. DOI:

[18]

Hung Dang, Tien Tuan Anh Dinh, Dumitrel Loghin, Ee-Chien Chang, Qian Lin, and Beng Chin Ooi. 2019. Towards scaling blockchain systems via sharding. In International Conference on Management of Data (SIGMOD). ACM, New York, USA, 123–140.

Digital Library

[19]

Sergi Delgado-Segura, Cristian Tanas, and Jordi Herrera-Joancomartí. 2016. Reputation and reward: Two sides of the same bitcoin. Sensors 16, 6 (2016), 776.

[20]

Ningning Ding, Zhixuan Fang, and Jianwei Huang. 2020. Optimal contract design for efficient federated learning with multi-dimensional private information. IEEE Journal on Selected Areas in Communications (JSAC) 39, 1 (2020), 186–200.

Digital Library

[21]

Saulo Dos Santos, Japjeet Singh, Ruppa K. Thulasiram, Shahin Kamali, Louis Sirico, and Lisa Loud. 2022. A new era of blockchain-powered decentralized finance (DeFi) - a review. In IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC). IEEE, New York, USA, 1286–1292.

[22]

Maya Dotan, Yvonne-Anne Pignolet, Stefan Schmid, Saar Tochner, and Aviv Zohar. 2021. Survey on blockchain networking: Context, state-of-the-art, challenges. ACM Computing Surveys (CSUR) 54, 5 (2021), 1–34.

Digital Library

[23]

Cynthia Dwork. 2008. Differential privacy: A survey of results. In International Conference on Theory and Applications of Models of Computation (TAMC). Springer, Berlin, Germany, 1–19.

[24]

Chaosheng Feng, Bin Liu, Keping Yu, Sotirios K. Goudos, and Shaohua Wan. 2021. Blockchain-empowered decentralized horizontal federated learning for 5G-enabled UAVs. IEEE Transactions on Industrial Informatics (TII) 18, 5 (2021), 3582–3592.

[25]

Andreas Geiger, Philip Lenz, and Raquel Urtasun. 2012. Are we ready for autonomous driving? The KITTI vision benchmark suite. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, New York, USA, 3354–3361.

Digital Library

[26]

Robin C. Geyer, Tassilo Klein, and Moin Nabi. 2017. Differentially private federated learning: A client level perspective. CoRR abs/1712.07557 (2017), 1–7.

[27]

Filip Hanzely, Slavomír Hanzely, Samuel Horváth, and Peter Richtárik. 2020. Lower bounds and optimal algorithms for personalized federated learning. In Annual Conference on Neural Information Processing Systems (NeurIPS). Curran Associates, Inc., Long Beach, CA, 2304–2315.

[28]

Gaofeng Hua, Li Zhu, Jinsong Wu, Chunzi Shen, Linyan Zhou, and Qingqing Lin. 2020. Blockchain-based federated learning for intelligent control in heavy haul railway. IEEE Access 8 (2020), 176830–176839.

[29]

Ling Huang, Anthony D. Joseph, Blaine Nelson, Benjamin I. P. Rubinstein, and J. Doug Tygar. 2011. Adversarial machine learning. In 4th ACM Workshop on Security and Artificial Intelligence. ACM, New York, USA, 43–58.

[30]

Li Huang, Yifeng Yin, Zeng Fu, Shifa Zhang, Hao Deng, and Dianbo Liu. 2018. LoAdaBoost: Loss-based AdaBoost federated machine learning on medical data. CoRR abs/1811.12629 (2018), 1–16.

[31]

Eunjeong Jeong, Seungeun Oh, Hyesung Kim, Jihong Park, Mehdi Bennis, and Seong-Lyun Kim. 2018. Communication-efficient on-device machine learning: Federated distillation and augmentation under non-IID private data. CoRR abs/1811.11479 (2018), 1–6.

[32]

Bin Jia, Xiaosong Zhang, Jiewen Liu, Yang Zhang, Ke Huang, and Yongquan Liang. 2021. Blockchain-enabled federated learning data protection aggregation scheme with differential privacy and homomorphic encryption in IIoT. IEEE Transactions on Industrial Informatics (TII) 18, 6 (2021), 4049–4058.

[33]

Shan Jiang, Jiannong Cao, Julie A. McCann, Yanni Yang, Yang Liu, Xiaoqing Wang, and Yuming Deng. 2019. Privacy-preserving and efficient multi-keyword search over encrypted data on blockchain. In IEEE International Conference on Blockchain (Blockchain). IEEE, New York, USA, 405–410.

[34]

Shan Jiang, Jiannong Cao, Hanqing Wu, and Yanni Yang. 2020. Fairness-based packing of industrial IoT data in permissioned blockchains. IEEE Transactions on Industrial Informatics (TII) 17, 11 (2020), 7639–7649.

[35]

Shan Jiang, Jiannong Cao, Hanqing Wu, Yanni Yang, Mingyu Ma, and Jianfei He. 2018. BlocHIE: A blockchain-based platform for healthcare information exchange. In IEEE International Conference on Smart Computing (SMARTCOMP). IEEE, New York, USA, 49–56.

[36]

Jiawen Kang, Zehui Xiong, Dusit Niyato, Shengli Xie, and Junshan Zhang. 2019. Incentive mechanism for reliable federated learning: A joint optimization approach to combining reputation and contract theory. IEEE Internet of Things Journal (IoT-J) 6, 6 (2019), 10700–10714.

[37]

Jiawen Kang, Zehui Xiong, Dusit Niyato, Dongdong Ye, Dong In Kim, and Jun Zhao. 2019. Toward secure blockchain-enabled internet of vehicles: Optimizing consensus management using reputation and contract theory. IEEE Transactions on Vehicular Technology (TVT) 68, 3 (2019), 2906–2920.

[38]

Jiawen Kang, Zehui Xiong, Dusit Niyato, Yuze Zou, Yang Zhang, and Mohsen Guizani. 2020. Reliable federated learning for mobile networks. IEEE Wireless Communications 27, 2 (2020), 72–80.

[39]

Jiawen Kang, Rong Yu, Xumin Huang, Maoqiang Wu, Sabita Maharjan, Shengli Xie, and Yan Zhang. 2018. Blockchain for secure and efficient data sharing in vehicular edge computing and networks. IEEE Internet of Things Journal (IoT-J) 6, 3 (2018), 4660–4670.

[40]

Latif U. Khan, Shashi Raj Pandey, Nguyen H. Tran, Walid Saad, Zhu Han, Minh N. H. Nguyen, and Choong Seon Hong. 2020. Federated learning for edge networks: Resource optimization and incentive mechanism. IEEE Communications Magazine 58, 10 (2020), 88–93.

[41]

Mikhail Khodak, Maria-Florina F. Balcan, and Ameet S. Talwalkar. 2019. Adaptive gradient-based meta-learning methods. In Advances in Neural Information Processing Systems (NeurIPS). Curran Associates, Inc., New York, USA, 5917–5928.

[42]

Aggelos Kiayias, Alexander Russell, Bernardo David, and Roman Oliynykov. 2017. Ouroboros: A provably secure proof-of-stake blockchain protocol. In Annual International Cryptology Conference (CRYPTO). Springer, Berlin, Germany, 357–388.

[43]

Hyesung Kim, Jihong Park, Mehdi Bennis, and Seong-Lyun Kim. 2019. Blockchained on-device federated learning. IEEE Communications Letters 24, 6 (2019), 1279–1283.

[44]

Eleftherios Kokoris-Kogias, Philipp Jovanovic, Linus Gasser, Nicolas Gailly, Ewa Syta, and Bryan Ford. 2018. OmniLedger: A secure, scale-out, decentralized ledger via sharding. In IEEE Symposium on Security and Privacy (SP). IEEE, New York, USA, 583–598.

[45]

Rajesh Kumar, Abdullah Aman Khan, Jay Kumar, Noorbakhsh Amiri Golilarz, Simin Zhang, Yang Ting, Chengyu Zheng, Wenyong Wang, et al. 2021. Blockchain-federated-learning and deep learning models for covid-19 detection using CT imaging. IEEE Sensors Journal 21, 14 (2021), 16301–16314.

[46]

Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. 2015. Deep learning. Nature 521, 7553 (2015), 436–444.

[47]

Dun Li, Dezhi Han, Tien-Hsiung Weng, Zibin Zheng, Hongzhi Li, Han Liu, Arcangelo Castiglione, and Kuan-Ching Li. 2022. Blockchain for federated learning toward secure distributed machine learning systems: A systemic survey. Soft Computing 26, 9 (2022), 4423–4440.

Digital Library

[48]

Peng Li and Song Guo. 2015. Incentive mechanisms for device-to-device communications. IEEE Network 29, 4 (2015), 75–79.

Digital Library

[49]

Tian Li, Shengyuan Hu, Ahmad Beirami, and Virginia Smith. 2021. Ditto: Fair and robust federated learning through personalization. In International Conference on Machine Learning (ICML). PMLR, 6357–6368.

[50]

Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith. 2020. Federated optimization in heterogeneous networks. Third Conference on Machine Learning and Systems (MLSys) 2 (2020), 429–450.

[51]

Xiaoqi Li, Peng Jiang, Ting Chen, Xiapu Luo, and Qiaoyan Wen. 2020. A survey on the security of blockchain systems. Future Generation Computer Systems (FGCS) 107 (2020), 841–853.

Digital Library

[52]

Xi Li, Zehua Wang, Victor C. M. Leung, Hong Ji, Yiming Liu, and Heli Zhang. 2021. Blockchain-empowered data-driven networks: A survey and outlook. ACM Computing Surveys (CSUR) 54, 3 (2021), 1–38.

Digital Library

[53]

Yuzheng Li, Chuan Chen, Nan Liu, Huawei Huang, Zibin Zheng, and Qiang Yan. 2020. A blockchain-based decentralized federated learning framework with committee consensus. IEEE Network 35, 1 (2020), 234–241.

Digital Library

[54]

Wei Yang Bryan Lim, Nguyen Cong Luong, Dinh Thai Hoang, Yutao Jiao, Ying-Chang Liang, Qiang Yang, Dusit Niyato, and Chunyan Miao. 2020. Federated learning in mobile edge networks: A comprehensive survey. IEEE Communications Surveys & Tutorials 22, 3 (2020), 2031–2063.

[55]

Hong Liu, Shuaipeng Zhang, Pengfei Zhang, Xinqiang Zhou, Xuebin Shao, Geguang Pu, and Yan Zhang. 2021. Blockchain and federated learning for collaborative intrusion detection in vehicular edge computing. IEEE Transactions on Vehicular Technology (TVT) 70, 6 (2021), 6073–6084.

[56]

Jiaqi Liu, Wei Wang, Deng Li, Shaohua Wan, and Hui Liu. 2019. Role of gifts in decision making: An endowment effect incentive mechanism for offloading in the IoV. IEEE Internet of Things Journal (IoT-J) 6, 4 (2019), 6933–6951.

[57]

Sin Kit Lo, Yue Liu, Qinghua Lu, Chen Wang, Xiwei Xu, Hye-Young Paik, and Liming Zhu. 2022. Towards trustworthy AI: Blockchain-based architecture design for accountability and fairness of federated learning systems. IEEE Internet of Things Journal (IoT-J) 5 (2022), 1–8.

[58]

Yunlong Lu, Xiaohong Huang, Yueyue Dai, Sabita Maharjan, and Yan Zhang. 2019. Blockchain and federated learning for privacy-preserved data sharing in industrial IoT. IEEE Transactions on Industrial Informatics (TII) 16, 6 (2019), 4177–4186.

[59]

Yunlong Lu, Xiaohong Huang, Ke Zhang, Sabita Maharjan, and Yan Zhang. 2020. Blockchain and federated learning for 5G beyond. IEEE Network 35, 1 (2020), 219–225.

Digital Library

[60]

Yunlong Lu, Xiaohong Huang, Ke Zhang, Sabita Maharjan, and Yan Zhang. 2020. Blockchain empowered asynchronous federated learning for secure data sharing in internet of vehicles. IEEE Transactions on Vehicular Technology (TVT) 69, 4 (2020), 4298–4311.

[61]

Yunlong Lu, Xiaohong Huang, Ke Zhang, Sabita Maharjan, and Yan Zhang. 2020. Low-latency federated learning and blockchain for edge association in digital twin empowered 6G networks. IEEE Transactions on Industrial Informatics (TII) 17, 7 (2020), 5098–5107.

[62]

Yuan Lu, Qiang Tang, and Guiling Wang. 2018. On enabling machine learning tasks atop public blockchains: A crowdsourcing approach. In IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, New York, USA, 81–88.

[63]

Nguyen Cong Luong, Zehui Xiong, Ping Wang, and Dusit Niyato. 2018. Optimal auction for edge computing resource management in mobile blockchain networks: A deep learning approach. In IEEE International Conference on Communications (ICC). IEEE, New York, USA, 1–6.

[64]

Loi Luu, Viswesh Narayanan, Chaodong Zheng, Kunal Baweja, Seth Gilbert, and Prateek Saxena. 2016. A secure sharding protocol for open blockchains. In ACM SIGSAC Conference on Computer and Communications Security (CCS). ACM, New York, USA, 17–30.

Digital Library

[65]

Umer Majeed and Choong Seon Hong. 2019. EFLChain: Ensemble learning via federated learning over blockchain network: A framework. In Proceedings of the KIISE Korea Computer Congress. 845–847.

[66]

Umer Majeed and Choong Seon Hong. 2019. FLchain: Federated learning via MEC-enabled blockchain network. In 20th Asia-Pacific Network Operations and Management Symposium (APNOMS). IEEE, New York, USA, 1–4.

[67]

Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. 2017. Communication-efficient learning of deep networks from decentralized data. In International Conference on Artificial Intelligence and Statistics (AISTATS). PMLR, 1273–1282.

[68]

H. Brendan McMahan, Daniel Ramage, Kunal Talwar, and Li Zhang. 2018. Learning differentially private recurrent language models. In 6th International Conference on Learning Representations (ICLR). OpenReview.net, 1–14.

[69]

Anudit Nagar. 2019. Privacy-preserving blockchain based federated learning with differential data sharing. CoRR abs/1912.04859 (2019), 1–9.

[70]

Dinh C. Nguyen, Ming Ding, Quoc-Viet Pham, Pubudu N. Pathirana, Long Bao Le, Aruna Seneviratne, Jun Li, Dusit Niyato, and H. Vincent Poor. 2021. Federated learning meets blockchain in edge computing: Opportunities and challenges. IEEE Internet of Things Journal (IoT-J) 8, 16 (2021), 12806–12825.

[71]

Takayuki Nishio and Ryo Yonetani. 2019. Client selection for federated learning with heterogeneous resources in mobile edge. In IEEE International Conference on Communications (ICC). IEEE, New York, USA, 1–7.

[72]

Safa Otoum, Ismaeel Al Ridhawi, and Hussein Mouftah. 2021. Securing critical IoT infrastructures with blockchain-supported federated learning. IEEE Internet of Things Journal (IoT-J) 9, 4 (2021), 2592–2601.

[73]

Daniel W. Otter, Julian R. Medina, and Jugal K. Kalita. 2020. A survey of the usages of deep learning for natural language processing. IEEE Transactions on Neural Networks and Learning Systems (TNNLS) 32, 2 (2020), 604–624.

[74]

Jonathan Passerat-Palmbach, Tyler Farnan, Robert Miller, Marielle S. Gross, Heather Leigh Flannery, and Bill Gleim. 2019. A blockchain-orchestrated federated learning architecture for healthcare consortia. CoRR abs/1910.12603 (2019), 1–6.

[75]

Zhe Peng, Jianliang Xu, Xiaowen Chu, Shang Gao, Yuan Yao, Rong Gu, and Yuzhe Tang. 2021. Vfchain: Enabling verifiable and auditable federated learning via blockchain systems. IEEE Transactions on Network Science and Engineering (TNSE) 9, 1 (2021), 173–186.

[76]

Shiva Raj Pokhrel and Jinho Choi. 2020. Federated learning with blockchain for autonomous vehicles: Analysis and design challenges. IEEE Transactions on Communications (TCOM) 68, 8 (2020), 4734–4746.

[77]

Dawid Połap, Gautam Srivastava, and Keping Yu. 2021. Agent architecture of an intelligent medical system based on federated learning and blockchain technology. Journal of Information Security and Applications (JISA) 58 (2021), 102748.

[78]

Davy Preuveneers, Vera Rimmer, Ilias Tsingenopoulos, Jan Spooren, Wouter Joosen, and Elisabeth Ilie-Zudor. 2018. Chained anomaly detection models for federated learning: An intrusion detection case study. Applied Sciences 8, 12 (2018), 2663.

[79]

Guanjin Qu, Naichuan Cui, Huaming Wu, Ruidong Li, and Yuemin Ding. 2021. ChainFL: A simulation platform for joint federated learning and blockchain in edge/cloud computing environments. IEEE Transactions on Industrial Informatics (TII) 18, 5 (2021), 3572–3581.

[80]

Xidi Qu, Shengling Wang, Qin Hu, and Xiuzhen Cheng. 2021. Proof of federated learning: A novel energy-recycling consensus algorithm. IEEE Transactions on Parallel and Distributed Systems (TPDS) 32, 8 (2021), 2074–2085.

Digital Library

[81]

Youyang Qu, Longxiang Gao, Tom H. Luan, Yong Xiang, Shui Yu, Bai Li, and Gavin Zheng. 2020. Decentralized privacy using blockchain-enabled federated learning in fog computing. IEEE Internet of Things Journal (IoT-J) 7, 6 (2020), 5171–5183.

[82]

Youyang Qu, Shiva Raj Pokhrel, Sahil Garg, Longxiang Gao, and Yong Xiang. 2020. A blockchained federated learning framework for cognitive computing in industry 4.0 networks. IEEE Transactions on Industrial Informatics (TII) 17, 4 (2020), 2964–2973.

[83]

Youyang Qu, Md Palash Uddin, Chenquan Gan, Yong Xiang, Longxiang Gao, and John Yearwood. 2022. Blockchain-enabled federated learning: A survey. ACM Computing Surveys (CSUR) 55 (2022), 1–33. https://doi.org/10.1145/3524104

Digital Library

[84]

Mohamed Abdur Rahman, M. Shamim Hossain, Mohammad Saiful Islam, Nabil A. Alrajeh, and Ghulam Muhammad. 2020. Secure and provenance enhanced internet of health things framework: A blockchain managed federated learning approach. IEEE Access 8 (2020), 205071–205087.

[85]

Paritosh Ramanan and Kiyoshi Nakayama. 2020. BAFFLE: Blockchain based aggregator free federated learning. In IEEE International Conference on Blockchain (Blockchain). IEEE, New York, USA, 72–81.

[86]

Paul Resnick and Hal R. Varian. 1997. Recommender systems. Communications of the ACM (CACM) 40, 3 (1997), 56–58.

Digital Library

[87]

Muhammad Shayan, Clement Fung, Chris J. M. Yoon, and Ivan Beschastnikh. 2020. Biscotti: A blockchain system for private and secure federated learning. IEEE Transactions on Parallel and Distributed Systems (TPDS) 32, 7 (2020), 1513–1525.

[88]

Meng Shen, Huan Wang, Bin Zhang, Liehuang Zhu, Ke Xu, Qi Li, and Xiaojiang Du. 2020. Exploiting unintended property leakage in blockchain-assisted federated learning for intelligent edge computing. IEEE Internet of Things Journal (IoT-J) 8, 4 (2020), 2265–2275.

[89]

Saurabh Singh, Shailendra Rathore, Osama Alfarraj, Amr Tolba, and Byungun Yoon. 2022. A framework for privacy-preservation of IoT healthcare data using federated learning and blockchain technology. Future Generation Computer Systems (FGCS) 129 (2022), 380–388.

Digital Library

[90]

Virginia Smith, Chao-Kai Chiang, Maziar Sanjabi, and Ameet S. Talwalkar. 2017. Federated multi-task learning. In Advances in Neural Information Processing Systems (NeurIPS). Curran Associates, Inc., New York, USA, 4424–4434.

[91]

Tianshu Song, Yongxin Tong, and Shuyue Wei. 2019. Profit allocation for federated learning. In IEEE International Conference on Big Data (BigData). IEEE, New York, USA, 2577–2586.

[92]

Gian Antonio Susto, Andrea Schirru, Simone Pampuri, Seán McLoone, and Alessandro Beghi. 2014. Machine learning for predictive maintenance: A multiple classifier approach. IEEE Transactions on Industrial Informatics (TII) 11, 3 (2014), 812–820.

[93]

Richard S. Sutton and Andrew G. Barto. 1998. Reinforcement learning: An introduction. IEEE Transactions on Neural Networks (TNN) 9, 5 (1998), 1054–1054.

Digital Library

[94]

Palina Tolmach, Yi Li, Shang-Wei Lin, Yang Liu, and Zengxiang Li. 2021. A survey of smart contract formal specification and verification. ACM Computing Surveys (CSUR) 54, 7 (2021), 1–38.

Digital Library

[95]

Kentaroh Toyoda and Allan N. Zhang. 2019. Mechanism design for an incentive-aware blockchain-enabled federated learning platform. In IEEE International Conference on Big Data (Big Data). IEEE, New York, USA, 395–403.

[96]

Florian Tramèr, Fan Zhang, Ari Juels, Michael K. Reiter, and Thomas Ristenpart. 2016. Stealing machine learning models via prediction APIs. In 25th USENIX Security Symposium. USENIX Association, California, USA, 601–618.

[97]

Nguyen H. Tran, Wei Bao, Albert Zomaya, Minh N. H. Nguyen, and Choong Seon Hong. 2019. Federated learning over wireless networks: Optimization model design and analysis. In IEEE Conference on Computer Communications (INFOCOM). IEEE, New York, USA, 1387–1395.

Digital Library

[98]

Stacey Truex, Nathalie Baracaldo, Ali Anwar, Thomas Steinke, Heiko Ludwig, Rui Zhang, and Yi Zhou. 2019. A hybrid approach to privacy-preserving federated learning. In 12th ACM Workshop on Artificial Intelligence and Security. ACM, New York, USA, 1–11.

[99]

Muhammad Habib ur Rehman, Khaled Salah, Ernesto Damiani, and Davor Svetinovic. 2020. Towards blockchain-based reputation-aware federated learning. In IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). IEEE, New York, USA, 183–188.

[100]

C. H. Van Berkel. 2009. Multi-core for mobile phones. In Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE, New York, USA, 1260–1265.

[101]

Paul Vanhaesebrouck, Aurélien Bellet, and Marc Tommasi. 2017. Decentralized collaborative learning of personalized models and collaboration graph. In International Conference on Artificial Intelligence and Statistics (AISTATS). PMLR, 509–517.

[102]

Omar Abdel Wahab, Azzam Mourad, Hadi Otrok, and Tarik Taleb. 2021. Federated machine learning: Survey, multi-level classification, desirable criteria and future directions in communication and networking systems. IEEE Communications Surveys & Tutorials 23, 2 (2021), 1342–1397.

[103]

Senzhang Wang, Jiannong Cao, and Philip S. Yu. 2022. Deep learning for spatio-temporal data mining: A survey. IEEE Trans. Knowl. Data Eng. 34, 8 (2022), 3681–3700. DOI:

[104]

Taotao Wang, Soung Chang Liew, and Shengli Zhang. 2021. When blockchain meets AI: Optimal mining strategy achieved by machine learning. International Journal of Intelligent Systems 36, 5 (2021), 2183–2207.

Digital Library

[105]

Tao Wang, Yunjian Xu, Chathura Withanage, Lan Lan, Selin Damla Ahipaşaoğlu, and Costas A. Courcoubetis. 2016. A fair and budget-balanced incentive mechanism for energy management in buildings. IEEE Transactions on Smart Grid (TSG) 9, 4 (2016), 3143–3153.

[106]

Yi Wang, Qixin Chen, Dahua Gan, Jingwei Yang, Daniel S. Kirschen, and Chongqing Kang. 2018. Deep learning-based socio-demographic information identification from smart meter data. IEEE Transactions on Smart Grid (TSG) 10, 3 (2018), 2593–2602.

[107]

Yuntao Wang, Zhou Su, Ning Zhang, and Abderrahim Benslimane. 2020. Learning in the air: Secure federated learning for UAV-assisted crowdsensing. IEEE Transactions on Network Science and Engineering (TNSE) 8, 2 (2020), 1055–1069.

[108]

Zhengwei Wang, Qi She, and Tomas E. Ward. 2021. Generative adversarial networks in computer vision: A survey and taxonomy. ACM Computing Surveys (CSUR) 54, 2 (2021), 1–38.

Digital Library

[109]

Kang Wei, Jun Li, Ming Ding, Chuan Ma, Howard H. Yang, Farhad Farokhi, Shi Jin, Tony Q. S. Quek, and H. Vincent Poor. 2020. Federated learning with differential privacy: Algorithms and performance analysis. IEEE Transactions on Information Forensics and Security (TIFS) 15 (2020), 3454–3469.

Digital Library

[110]

Jiasi Weng, Jian Weng, Jilian Zhang, Ming Li, Yue Zhang, and Weiqi Luo. 2019. DeepChain: Auditable and privacy-preserving deep learning with blockchain-based incentive. IEEE Transactions on Dependable and Secure Computing (TDSC) 18, 5 (2019), 2438–2455.

[111]

Hanqing Wu, Jiannong Cao, Yanni Yang, Cheung Leong Tung, Shan Jiang, Bin Tang, Yang Liu, Xiaoqing Wang, and Yuming Deng. 2019. Data management in supply chain using blockchain: Challenges and a case study. In 28th International Conference on Computer Communication and Networks (ICCCN). IEEE, New York, USA, 1–8.

[112]

Hanqing Wu, Shan Jiang, and Jiannong Cao. 2022. High-efficiency blockchain-based supply chain traceability. IEEE Transactions on Intelligent Transportation Systems (T-ITS). Early Access (2022), 1–11.

[113]

Peng Xiao, Samuel Cheng, Vladimir Stankovic, and Dejan Vukobratovic. 2020. Averaging is probably not the optimum way of aggregating parameters in federated learning. Entropy 22, 3 (2020), 314.

[114]

Guowen Xu, Hongwei Li, Sen Liu, Kan Yang, and Xiaodong Lin. 2019. VerifyNet: Secure and verifiable federated learning. IEEE Transactions on Information Forensics and Security (TIFS) 15 (2019), 911–926.

Digital Library

[115]

Jia Xu, Jinxin Xiang, and Dejun Yang. 2015. Incentive mechanisms for time window dependent tasks in mobile crowdsensing. IEEE Transactions on Wireless Communications (TWC) 14, 11 (2015), 6353–6364.

Digital Library

[116]

Qiang Yang, Yang Liu, Tianjian Chen, and Yongxin Tong. 2019. Federated machine learning: Concept and applications. ACM Transactions on Intelligent Systems and Technology (TIST) 10, 2 (2019), 1–19.

Digital Library

[117]

Dong Yin, Yudong Chen, Ramchandran Kannan, and Peter Bartlett. 2018. Byzantine-robust distributed learning: Towards optimal statistical rates. In International Conference on Machine Learning (ICML). PMLR, 5650–5659.

[118]

Mahdi Zamani, Mahnush Movahedi, and Mariana Raykova. 2018. RapidChain: Scaling blockchain via full sharding. In ACM SIGSAC Conference on Computer and Communications Security (CCS). ACM, New York, USA, 931–948.

Digital Library

[119]

Rongfei Zeng, Shixun Zhang, Jiaqi Wang, and Xiaowen Chu. 2020. FMore: An incentive scheme of multi-dimensional auction for federated learning in MEC. In IEEE 40th International Conference on Distributed Computing Systems (ICDCS). IEEE, New York, USA, 278–288.

[120]

Yufeng Zhan, Peng Li, and Song Guo. 2020. Experience-driven computational resource allocation of federated learning by deep reinforcement learning. In IEEE International Parallel and Distributed Processing Symposium (IPDPS). IEEE, New York, USA, 234–243.

[121]

Yufeng Zhan, Peng Li, Kun Wang, Song Guo, and Yuanqing Xia. 2020. Big data analytics by CrowdLearning: Architecture and mechanism design. IEEE Network 34, 3 (2020), 143–147.

[122]

Yufeng Zhan, Yuanqing Xia, and Jinhui Zhang. 2018. Incentive mechanism in platform-centric mobile crowdsensing: A one-to-many bargaining approach. Computer Networks 132 (2018), 40–52.

Digital Library

[123]

Chen Zhang, Yu Xie, Hang Bai, Bin Yu, Weihong Li, and Yuan Gao. 2021. A survey on federated learning. Knowledge-Based Systems (KBS) 216 (2021), 106775.

[124]

Jiale Zhang, Bing Chen, Shui Yu, and Hai Deng. 2019. PEFL: A privacy-enhanced federated learning scheme for big data analytics. In IEEE Global Communications Conference (GLOBECOM). IEEE, New York, USA, 1–6.

Digital Library

[125]

Jingwen Zhang, Yuezhou Wu, and Rong Pan. 2021. Incentive mechanism for horizontal federated learning based on reputation and reverse auction. In The Web Conference (WWW). ACM, New York, USA, 947–956.

Digital Library

[126]

Peiying Zhang, Yanrong Hong, Neeraj Kumar, Mamoun Alazab, Mohammad Dahman Alshehri, and Chunxiao Jiang. 2021. BC-EdgeFL: A defensive transmission model based on blockchain-assisted reinforced federated learning in IIoT environment. IEEE Transactions on Industrial Informatics (TII) 18, 5 (2021), 3551–3561.

[127]

Weishan Zhang, Qinghua Lu, Qiuyu Yu, Zhaotong Li, Yue Liu, Sin Kit Lo, Shiping Chen, Xiwei Xu, and Liming Zhu. 2020. Blockchain-based federated learning for device failure detection in industrial IoT. IEEE Internet of Things Journal (IoT-J) 8, 7 (2020), 5926–5937.

[128]

Zhebin Zhang, Dajie Dong, Yuhang Ma, Yilong Ying, Dawei Jiang, Ke Chen, Lidan Shou, and Gang Chen. 2021. Refiner: A reliable incentive-driven federated learning system powered by blockchain. Proceedings of the VLDB Endowment (PVLDB) 14, 12 (2021), 2659–2662.

Digital Library

[129]

Bo Zhao, Konda Reddy Mopuri, and Hakan Bilen. 2020. iDLG: Improved deep leakage from gradients. CoRR abs/2001.02610 (2020), 1–5.

[130]

Yang Zhao, Jun Zhao, Linshan Jiang, Rui Tan, Dusit Niyato, Zengxiang Li, Lingjuan Lyu, and Yingbo Liu. 2020. Privacy-preserving blockchain-based federated learning for IoT devices. IEEE Internet of Things Journal (IoT-J) 8, 3 (2020), 1817–1829.

[131]

Yang Zhao, Jun Zhao, Linshan Jiang, Rui Tan, Dusit Niyato, Zengxiang Li, Lingjuan Lyu, and Yingbo Liu. 2020. Privacy-preserving blockchain-based federated learning for IoT devices. IEEE Internet of Things Journal (IoT-J) 8, 3 (2020), 1817–1829.

[132]

Zibin Zheng, Shaoan Xie, Hong-Ning Dai, Xiangping Chen, and Huaimin Wang. 2018. Blockchain challenges and opportunities: A survey. International Journal of Web and Grid Services (IJWGS) 14, 4 (2018), 352–375.

[133]

Sicong Zhou, Huawei Huang, Wuhui Chen, Pan Zhou, Zibin Zheng, and Song Guo. 2020. PiRATE: A blockchain-based secure framework of distributed machine learning in 5G networks. IEEE Network 34, 6 (2020), 84–91.

Digital Library

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cover image ACM Computing Surveys

ACM Computing Surveys Volume 55, Issue 11

November 2023

849 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3572825

Editor:
Albert Zomaya
University of Sydney, Australia

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Publication History

Published: 22 February 2023

Online AM: 09 November 2022

Accepted: 01 November 2022

Revised: 06 July 2022

Received: 01 July 2021

Published in CSUR Volume 55, Issue 11

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https://dl.acm.org/doi/10.4018/IJSWIS.335495
Karydas DLeligou H(2024)Federated Learning: Attacks and Defenses, Rewards, Energy Efficiency: Past, Present and FutureWSEAS TRANSACTIONS ON COMPUTERS10.37394/23205.2024.23.1023(106-135)Online publication date: 12-Jun-2024
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(2024)Investigating the Simultaneous Performance of AI and Blockchain on E-banking TransactionInternational Journal of Material and Mathematical Sciences10.34104/ijmms.024.014021(14-21)Online publication date: 5-Mar-2024
https://doi.org/10.34104/ijmms.024.014021
Mengistu TKim TLin J(2024)A Survey on Heterogeneity Taxonomy, Security and Privacy Preservation in the Integration of IoT, Wireless Sensor Networks and Federated LearningSensors10.3390/s2403096824:3(968)Online publication date: 1-Feb-2024
https://doi.org/10.3390/s24030968
Kim YHong SMajer M(2024)Validation of Value-Driven Token Economy: Focus on Blockchain Content PlatformFuture Internet10.3390/fi1605017816:5(178)Online publication date: 20-May-2024
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Babar MQureshi BKoubaa A(2024)Investigating the impact of data heterogeneity on the performance of federated learning algorithm using medical imagingPLOS ONE10.1371/journal.pone.030253919:5(e0302539)Online publication date: 15-May-2024
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Belal YBen Mokhtar SHaddadi HWang JMashhadi A(2024)Survey of Federated Learning Models for Spatial-Temporal Mobility ApplicationsACM Transactions on Spatial Algorithms and Systems10.1145/366608910:3(1-39)Online publication date: 1-Jun-2024
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