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DID-eFed: Facilitating Federated Learning as a Service with Decentralized Identities

Published: 21 June 2021 Publication History

Abstract

We have entered the era of big data, and it is considered to be the ”fuel” for the flourishing of artificial intelligence applications. The enactment of the EU General Data Protection Regulation (GDPR) raises concerns about individuals’ privacy in big data. Federated learning (FL) emerges as a functional solution that can help build high-performance models shared among multiple parties while still complying with user privacy and data confidentiality requirements. Although FL has been intensively studied and used in real applications, there is still limited research related to its prospects and applications as a FLaaS (Federated Learning as a Service) to interested 3rd parties. In this paper, we present a FLaaS system: DID-eFed, where FL is facilitated by decentralized identities (DID) and a smart contract. DID enables a more flexible and credible decentralized access management in our system, while the smart contract offers a frictionless and less error-prone process. We describe particularly the scenario where our DID-eFed enables the FLaaS among hospitals and research institutions.

References

[1]
Oscar Avellaneda, Alan Bachmann, Abbie Barbir, Joni Brenan, Pamela Dingle, Kim Hamilton Duffy, Eve Maler, Drummond Reed, and Manu Sporny. 2019. Decentralized Identity: Where Did It Come From and Where Is It Going?IEEE Communications Standards Magazine 3, 4 (2019), 10–13.
[2]
Eugene Bagdasaryan, Andreas Veit, Yiqing Hua, Deborah Estrin, and Vitaly Shmatikov. 2020. How to backdoor federated learning. In International Conference on Artificial Intelligence and Statistics. PMLR, 2938–2948.
[3]
Mohammed Amine Bouras, Qinghua Lu, Fan Zhang, Yueliang Wan, Tao Zhang, and Huansheng Ning. 2020. Distributed ledger technology for eHealth identity privacy: State of the art and future perspective. Sensors 20, 2 (2020), 483.
[4]
Di Chai, Leye Wang, Kai Chen, and Qiang Yang. 2020. Secure federated matrix factorization. IEEE Intelligent Systems(2020).
[5]
Vikram Dhillon, David Metcalf, and Max Hooper. 2017. The hyperledger project. In Blockchain enabled applications. Springer, 139–149.
[6]
Xinxin Fan, Qi Chai, Lei Xu, and Dong Guo. 2020. DIAM-IoT: A Decentralized Identity and Access Management Framework for Internet of Things. In Proceedings of the 2nd ACM International Symposium on Blockchain and Secure Critical Infrastructure. 186–191.
[7]
Clement Fung, Chris JM Yoon, and Ivan Beschastnikh. 2018. Mitigating sybils in federated learning poisoning. arXiv preprint arXiv:1808.04866(2018).
[8]
Laura Garcia Cuenca, Javier Sanchez-Soriano, Enrique Puertas, Javier Fernandez Andres, and Nourdine Aliane. 2019. Machine learning techniques for undertaking roundabouts in autonomous driving. Sensors 19, 10 (2019), 2386.
[9]
Robin C Geyer, Tassilo Klein, and Moin Nabi. 2017. Differentially private federated learning: A client level perspective. arXiv preprint arXiv:1712.07557(2017).
[10]
WA Group 2020. Federated learning white paper v1. 0.
[11]
Andreas Grüner, Alexander Mühle, Tatiana Gayvoronskaya, and Christoph Meinel. 2019. A comparative analysis of trust requirements in decentralized identity management. In International Conference on Advanced Information Networking and Applications. Springer, Cham, 200–213.
[12]
Stephen Hardy, Wilko Henecka, Hamish Ivey-Law, Richard Nock, Giorgio Patrini, Guillaume Smith, and Brian Thorne. 2017. Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption. arXiv preprint arXiv:1711.10677(2017).
[13]
Justin D Harris and Bo Waggoner. 2019. Decentralized and collaborative AI on blockchain. In 2019 IEEE International Conference on Blockchain (Blockchain). IEEE, 368–375.
[14]
Vincent C Hu, D Richard Kuhn, David F Ferraiolo, and Jeffrey Voas. 2015. Attribute-based access control. Computer 48, 2 (2015), 85–88.
[15]
Ori Jacobovitz. 2016. Blockchain for identity management. The Lynne and William Frankel Center for Computer Science Department of Computer Science. Ben-Gurion University, Beer Sheva (2016).
[16]
Dhanya Therese Jose, Antorweep Chakravorty, and Chunming Rong. 2019. TOTEM: Token for controlled computation: Integrating Blockchain with Big Data. In 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT). IEEE, Piscataway, NJ, 1–7.
[17]
Jakub Konecný, Brendan McMahan, and Daniel Ramage. 2015. Federated Optimization: Distributed Optimization Beyond the Datacenter. In NIPS Optimization for Machine Learning Workshop. 5. http://arxiv.org/abs/1511.03575
[18]
Nicolas Kourtellis, Kleomenis Katevas, and Diego Perino. 2020. FLaaS: Federated Learning as a Service. In Proceedings of the 1st Workshop on Distributed Machine Learning. 7–13.
[19]
Suyi Li, Yong Cheng, Wei Wang, Yang Liu, and Tianjian Chen. 2020. Learning to detect malicious clients for robust federated learning. arXiv preprint arXiv:2002.00211(2020).
[20]
Xiang Li, Kaixuan Huang, Wenhao Yang, Shusen Wang, and Zhihua Zhang. 2019. On the convergence of fedavg on non-iid data. arXiv preprint arXiv:1907.02189(2019).
[21]
Angelica Nakagawa Lima, Eric Allison Philot, Gustavo Henrique Goulart Trossini, Luis Paulo Barbour Scott, Vinícius Gonçalves Maltarollo, and Kathia Maria Honorio. 2016. Use of machine learning approaches for novel drug discovery. Expert opinion on drug discovery 11, 3 (2016), 225–239.
[22]
Markus Luecking, Christian Fries, Robin Lamberti, and Wilhelm Stork. 2020. Decentralized identity and trust management framework for Internet of Things. In 2020 IEEE International Conference on Blockchain and Cryptocurrency (ICBC). IEEE, 1–9.
[23]
Loi Luu, Duc-Hiep Chu, Hrishi Olickel, Prateek Saxena, and Aquinas Hobor. 2016. Making smart contracts smarter. In Proceedings of the 2016 ACM SIGSAC conference on computer and communications security. 254–269.
[24]
Lingjuan Lyu, Han Yu, and Qiang Yang. 2020. Threats to federated learning: A survey. arXiv preprint arXiv:2003.02133(2020).
[25]
Deepak Maram, Harjasleen Malvai, Fan Zhang, Nerla Jean-Louis, Alexander Frolov, Tyler Kell, Tyrone Lobban, Christine Moy, Ari Juels, and Andrew Miller. 2020. CanDID: Can-Do Decentralized Identity with Legacy Compatibility, Sybil-Resistance, and Accountability. (2020).
[26]
Gihan J Mendis, Yifu Wu, Jin Wei, Moein Sabounchi, and Rigoberto Roche. 2020. A blockchain-powered decentralized and secure computing paradigm. IEEE Transactions on Emerging Topics in Computing (2020). https://doi.org/10.1109/TETC.2020.2983007
[27]
Satoshi Nakamoto and A Bitcoin. 2008. A peer-to-peer electronic cash system. Bitcoin.–URL: https://bitcoin. org/bitcoin. pdf 4 (2008).
[28]
Robert Philipp, Andreas Mladenow, Christine Strauss, and Alexander Völz. 2020. Machine Learning as a Service: Challenges in Research and Applications. In Proceedings of the 22nd International Conference on Information Integration and Web-based Applications & Services. 396–406.
[29]
Ladislav Rampasek and Anna Goldenberg. 2016. TensorFlow: biology’s gateway to deep learning?Cell systems 2, 1 (2016), 12–14.
[30]
Drummond Reed, Les Chasen, and Respect Network. 2016. Requirements for DIDs (Decentralized Identifiers). In Rebooting the Web of Trust II: ID2020 Design Workshop.
[31]
Sushmita Ruj, Milos Stojmenovic, and Amiya Nayak. 2013. Decentralized access control with anonymous authentication of data stored in clouds. IEEE transactions on parallel and distributed systems 25, 2 (2013), 384–394.
[32]
Mohamed Seif, Ravi Tandon, and Ming Li. 2020. Wireless federated learning with local differential privacy. In 2020 IEEE International Symposium on Information Theory (ISIT). IEEE, 2604–2609.
[33]
Ziteng Sun, Peter Kairouz, Ananda Theertha Suresh, and H Brendan McMahan. 2019. Can you really backdoor federated learning?arXiv preprint arXiv:1911.07963(2019).
[34]
Stacey Truex, Ling Liu, Ka-Ho Chow, Mehmet Emre Gursoy, and Wenqi Wei. 2020. LDP-Fed: Federated learning with local differential privacy. In Proceedings of the Third ACM International Workshop on Edge Systems, Analytics and Networking. 61–66.
[35]
Andrei Volkov. 2020. Addressing the Challenges Facing Decentralized Iden-tity Systems. EDITORIAL–From the Faculty Editor(2020), 10.
[36]
Shangping Wang, Yinglong Zhang, and Yaling Zhang. 2018. A blockchain-based framework for data sharing with fine-grained access control in decentralized storage systems. Ieee Access 6(2018), 38437–38450.
[37]
Kang Wei, Jun Li, Ming Ding, Chuan Ma, Howard H Yang, Farhad Farokhi, Shi Jin, Tony QS Quek, and H Vincent Poor. 2020. Federated learning with differential privacy: Algorithms and performance analysis. IEEE Transactions on Information Forensics and Security 15 (2020), 3454–3469.
[38]
D. Wood. 2014. ETHEREUM: A SECURE DECENTRALISED GENERALISED TRANSACTION LEDGER.
[39]
QI Xia, Emmanuel Boateng Sifah, Kwame Omono Asamoah, Jianbin Gao, Xiaojiang Du, and Mohsen Guizani. 2017. MeDShare: Trust-less medical data sharing among cloud service providers via blockchain. IEEE Access 5(2017), 14757–14767.
[40]
Chulin Xie, Keli Huang, Pin-Yu Chen, and Bo Li. 2019. Dba: Distributed backdoor attacks against federated learning. In International Conference on Learning Representations.
[41]
Qiang Yang, Yang Liu, Yong Cheng, Yan Kang, Tianjian Chen, and Han Yu. 2019. Federated learning. Synthesis Lectures on Artificial Intelligence and Machine Learning 13, 3(2019), 1–207.
[42]
Andrew C Yao. 1982. Protocols for secure computations. In 23rd annual symposium on foundations of computer science (sfcs 1982). IEEE, 160–164.
[43]
Affan Yasin and Lin Liu. 2016. An online identity and smart contract management system. In 2016 IEEE 40th Annual Computer Software and Applications Conference (COMPSAC), Vol. 2. IEEE, 192–198.
[44]
Zhimin Yin, Xiangzhan Yu, and Hongli Zhang. 2014. Commodity recommendation algorithm based on social network. In Advances in Computer Science and its Applications. Springer, 27–33.
[45]
Jiale Zhang, Junyu Wang, Yanchao Zhao, and Bing Chen. 2019. An efficient federated learning scheme with differential privacy in mobile edge computing. In International Conference on Machine Learning and Intelligent Communications. Springer, 538–550.
[46]
Yang Zhao, Jun Zhao, Mengmeng Yang, Teng Wang, Ning Wang, Lingjuan Lyu, Dusit Niyato, and Kwok-Yan Lam. 2020. Local differential privacy based federated learning for Internet of Things. IEEE Internet of Things Journal(2020).

Cited By

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  • (2024)FLHB-AC: Federated Learning History-Based Access Control Using Deep Neural Networks in Healthcare SystemJournal of Information Systems and Telecommunication (JIST)10.61186/jist.44500.12.46.9012:46(90-104)Online publication date: 24-Jun-2024
  • (2024)Enabling Federated Learning at the Edge through the IOTA TangleFuture Generation Computer Systems10.1016/j.future.2023.10.014152:C(17-29)Online publication date: 4-Mar-2024
  • (2024)Balancing Privacy and Progress in Artificial Intelligence: Anonymization in Histopathology for Biomedical Research and EducationFrontiers of Artificial Intelligence, Ethics, and Multidisciplinary Applications10.1007/978-981-99-9836-4_31(417-429)Online publication date: 25-Feb-2024
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cover image ACM Other conferences
EASE '21: Proceedings of the 25th International Conference on Evaluation and Assessment in Software Engineering
June 2021
417 pages
ISBN:9781450390538
DOI:10.1145/3463274
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 June 2021

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Author Tags

  1. FLaaS
  2. blockchain
  3. decentralized identity
  4. federated learning

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  • Research-article
  • Research
  • Refereed limited

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  • H2020-MSCA-ITN-2019 CLARIFY

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EASE 2021

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Overall Acceptance Rate 71 of 232 submissions, 31%

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Cited By

View all
  • (2024)FLHB-AC: Federated Learning History-Based Access Control Using Deep Neural Networks in Healthcare SystemJournal of Information Systems and Telecommunication (JIST)10.61186/jist.44500.12.46.9012:46(90-104)Online publication date: 24-Jun-2024
  • (2024)Enabling Federated Learning at the Edge through the IOTA TangleFuture Generation Computer Systems10.1016/j.future.2023.10.014152:C(17-29)Online publication date: 4-Mar-2024
  • (2024)Balancing Privacy and Progress in Artificial Intelligence: Anonymization in Histopathology for Biomedical Research and EducationFrontiers of Artificial Intelligence, Ethics, and Multidisciplinary Applications10.1007/978-981-99-9836-4_31(417-429)Online publication date: 25-Feb-2024
  • (2023)Survey on Digital Sovereignty and Identity: From Digitization to DigitalizationACM Computing Surveys10.1145/361640056:3(1-36)Online publication date: 5-Oct-2023
  • (2023)TruFLaaS: Trustworthy Federated Learning as a ServiceIEEE Internet of Things Journal10.1109/JIOT.2023.328289910:24(21266-21281)Online publication date: 15-Dec-2023
  • (2022)OpenIaC: open infrastructure as code - the network is my computerJournal of Cloud Computing: Advances, Systems and Applications10.1186/s13677-022-00285-711:1Online publication date: 8-May-2022
  • (2022)FlowChain: The Playground for Federated Learning in Industrial Internet of Things EnvironmentsIEEE Internet of Things Magazine10.1109/IOTM.001.21001885:2(78-83)Online publication date: Jun-2022
  • (2022)Managing Digital Objects with Decentralised Identifiers based on NFT-like schema2022 IEEE International Conference on Cloud Computing Technology and Science (CloudCom)10.1109/CloudCom55334.2022.00042(246-251)Online publication date: Dec-2022
  • (2022)Blockchain Empowered and Self-sovereign Access Control System2022 IEEE International Conference on Cloud Computing Technology and Science (CloudCom)10.1109/CloudCom55334.2022.00021(74-82)Online publication date: Dec-2022
  • (2022)Blockchain-based Cross-organizational Workflow Platform2022 IEEE International Conference on Cloud Computing Technology and Science (CloudCom)10.1109/CloudCom55334.2022.00018(53-59)Online publication date: Dec-2022

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