Federated Learning Meets Blockchain in Edge Computing: Opportunities and Challenges

Mobile-edge computing (MEC) has been envisioned as a promising paradigm to handle the massive volume of data generated from ubiquitous mobile devices for enabling intelligent services with the help of artificial intelligence (AI). Traditionally, AI techniques often require centralized data collection and training in a single entity, e.g., an MEC server, which is now becoming a weak point due to data privacy concerns and high overhead of raw data communications. In this context, federated learning (FL) has been proposed to provide collaborative data training solutions, by coordinating multiple mobile devices to train a shared AI model without directly exposing their underlying data, which enjoys considerable privacy enhancement. To improve the security and scalability of FL implementation, blockchain as a ledger technology is attractive for realizing decentralized FL training without the need for any central server. Particularly, the integration of FL and blockchain leads to a new paradigm, called FLchain, which potentially transforms intelligent MEC networks into decentralized, secure, and privacy-enhancing systems. This article presents an overview of the fundamental concepts and explores the opportunities of FLchain in MEC networks. We identify several main issues in FLchain design, including communication cost, resource allocation, incentive mechanism, security and privacy protection. The key solutions and the lessons learned along with the outlooks are also discussed. Then, we investigate the applications of FLchain in popular MEC domains, such as edge data sharing, edge content caching and edge crowdsensing. Finally, important research challenges and future directions are also highlighted.

[1]  Mianxiong Dong,et al.  Learning IoT in Edge: Deep Learning for the Internet of Things with Edge Computing , 2018, IEEE Network.

[2]  Guanghui Wen,et al.  Incentivizing Honest Mining in Blockchain Networks: A Reputation Approach , 2020, IEEE Transactions on Circuits and Systems II: Express Briefs.

[3]  Vladimir Braverman,et al.  FetchSGD: Communication-Efficient Federated Learning with Sketching , 2022 .

[4]  Yue Zhang,et al.  DeepChain: Auditable and Privacy-Preserving Deep Learning with Blockchain-Based Incentive , 2019, IEEE Transactions on Dependable and Secure Computing.

[5]  Deze Zeng,et al.  A Learning-Based Incentive Mechanism for Federated Learning , 2020, IEEE Internet of Things Journal.

[6]  Bo Wang,et al.  Blockchain Networks as Adaptive Systems , 2019, 2019 IEEE/ACM 14th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS).

[7]  Yulei Wu,et al.  FDC: A Secure Federated Deep Learning Mechanism for Data Collaborations in the Internet of Things , 2020, IEEE Internet of Things Journal.

[8]  Jinyuan Jia,et al.  Local Model Poisoning Attacks to Byzantine-Robust Federated Learning , 2019, USENIX Security Symposium.

[9]  Jonathan Rodriguez,et al.  Robust Mobile Crowd Sensing: When Deep Learning Meets Edge Computing , 2018, IEEE Network.

[10]  Yonina C. Eldar,et al.  UVeQFed: Universal Vector Quantization for Federated Learning , 2020, IEEE Transactions on Signal Processing.

[11]  Zhenyu Liao,et al.  A Large Dimensional Analysis of Least Squares Support Vector Machines , 2017, IEEE Transactions on Signal Processing.

[12]  F. Richard Yu,et al.  Integrated Blockchain and Edge Computing Systems: A Survey, Some Research Issues and Challenges , 2019, IEEE Communications Surveys & Tutorials.

[13]  Seong-Lyun Kim,et al.  Blockchained On-Device Federated Learning , 2018, IEEE Communications Letters.

[14]  Fan Li,et al.  PoBT: A Lightweight Consensus Algorithm for Scalable IoT Business Blockchain , 2020, IEEE Internet of Things Journal.

[15]  Choong Seon Hong,et al.  FLchain: Federated Learning via MEC-enabled Blockchain Network , 2019, 2019 20th Asia-Pacific Network Operations and Management Symposium (APNOMS).

[16]  Yang Cao,et al.  Transparent Contribution Evaluation for Secure Federated Learning on Blockchain , 2021, 2021 IEEE 37th International Conference on Data Engineering Workshops (ICDEW).

[17]  Jun Zhao,et al.  Local Differential Privacy-Based Federated Learning for Internet of Things , 2020, IEEE Internet of Things Journal.

[18]  Jong-Hyouk Lee,et al.  Double-Spending With a Sybil Attack in the Bitcoin Decentralized Network , 2019, IEEE Transactions on Industrial Informatics.

[19]  Seong-Lyun Kim,et al.  Communication-Efficient and Distributed Learning Over Wireless Networks: Principles and Applications , 2020, Proceedings of the IEEE.

[20]  H. Poor,et al.  When Federated Learning Meets Blockchain: A New Distributed Learning Paradigm , 2020, IEEE Computational Intelligence Magazine.

[21]  Dinh C. Nguyen,et al.  Blockchain for 5G and Beyond Networks: A State of the Art Survey , 2019, J. Netw. Comput. Appl..

[22]  Lixing Yu,et al.  AI at the Edge: Blockchain-Empowered Secure Multiparty Learning With Heterogeneous Models , 2020, IEEE Internet of Things Journal.

[23]  Solmaz Niknam,et al.  Federated Learning for Wireless Communications: Motivation, Opportunities, and Challenges , 2019, IEEE Communications Magazine.

[24]  Choong Seon Hong,et al.  Blockchain-based Node-aware Dynamic Weighting Methods for Improving Federated Learning Performance , 2019, 2019 20th Asia-Pacific Network Operations and Management Symposium (APNOMS).

[25]  Klaus-Robert Müller,et al.  Robust and Communication-Efficient Federated Learning From Non-i.i.d. Data , 2019, IEEE Transactions on Neural Networks and Learning Systems.

[26]  Xiaohong Huang,et al.  Low-Latency Federated Learning and Blockchain for Edge Association in Digital Twin Empowered 6G Networks , 2020, IEEE Transactions on Industrial Informatics.

[27]  Dinh C. Nguyen,et al.  Blockchain and Edge Computing for Decentralized EMRs Sharing in Federated Healthcare , 2020, GLOBECOM 2020 - 2020 IEEE Global Communications Conference.

[28]  H. Vincent Poor,et al.  Enabling AI in Future Wireless Networks: A Data Life Cycle Perspective , 2020, IEEE Communications Surveys & Tutorials.

[29]  Aruna Seneviratne,et al.  BEdgeHealth: A Decentralized Architecture for Edge-Based IoMT Networks Using Blockchain , 2021, IEEE Internet of Things Journal.

[30]  Ke Zhang,et al.  Blockchain Empowered Asynchronous Federated Learning for Secure Data Sharing in Internet of Vehicles , 2020, IEEE Transactions on Vehicular Technology.

[31]  Milind Kulkarni,et al.  Survey of Personalization Techniques for Federated Learning , 2020, 2020 Fourth World Conference on Smart Trends in Systems, Security and Sustainability (WorldS4).

[32]  Peng Jiang,et al.  A Survey on the Security of Blockchain Systems , 2017, Future Gener. Comput. Syst..

[33]  Dongxi Liu,et al.  A Trustworthy Privacy Preserving Framework for Machine Learning in Industrial IoT Systems , 2020, IEEE Transactions on Industrial Informatics.

[34]  Jinho Choi,et al.  Federated Learning With Blockchain for Autonomous Vehicles: Analysis and Design Challenges , 2020, IEEE Transactions on Communications.

[35]  Tony Q. S. Quek,et al.  On Safeguarding Privacy and Security in the Framework of Federated Learning , 2019, IEEE Network.

[36]  Jorge Pena Queralta,et al.  Blockchain for Mobile Edge Computing: Consensus Mechanisms and Scalability , 2020, Mobile Edge Computing.

[37]  Yonina C. Eldar,et al.  Adaptive Quantization of Model Updates for Communication-Efficient Federated Learning , 2021, ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[38]  Ali Dehghantanha,et al.  A survey on security and privacy of federated learning , 2021, Future Gener. Comput. Syst..

[39]  Shengling Wang,et al.  Corking by Forking: Vulnerability Analysis of Blockchain , 2019, IEEE INFOCOM 2019 - IEEE Conference on Computer Communications.

[40]  Muhammad Khurram Khan,et al.  PoRX: A reputation incentive scheme for blockchain consensus of IIoT , 2020, Future Gener. Comput. Syst..

[41]  Yijin Chen,et al.  A Hierarchical Blockchain-Enabled Federated Learning Algorithm for Knowledge Sharing in Internet of Vehicles , 2021, IEEE Transactions on Intelligent Transportation Systems.

[42]  Anit Kumar Sahu,et al.  Federated Learning: Challenges, Methods, and Future Directions , 2019, IEEE Signal Processing Magazine.

[43]  Sameep Mehta,et al.  Ownership Preserving AI Market Places Using Blockchain , 2019, 2019 IEEE International Conference on Blockchain (Blockchain).

[44]  Aruna Seneviratne,et al.  Privacy-Preserved Task Offloading in Mobile Blockchain With Deep Reinforcement Learning , 2019, IEEE Transactions on Network and Service Management.

[45]  Shiho Moriai,et al.  Privacy-Preserving Deep Learning via Additively Homomorphic Encryption , 2018, IEEE Transactions on Information Forensics and Security.

[46]  Wei Yang Bryan Lim,et al.  Federated Learning in Mobile Edge Networks: A Comprehensive Survey , 2019, IEEE Communications Surveys & Tutorials.

[47]  Tharmalingam Ratnarajah,et al.  Online Content Popularity Prediction and Learning in Wireless Edge Caching , 2020, IEEE Transactions on Communications.

[48]  Yusheng Ji,et al.  Federated Learning for Vehicular Internet of Things: Recent Advances and Open Issues , 2020, IEEE Open Journal of the Computer Society.

[49]  Xu Chen,et al.  In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning , 2018, IEEE Network.

[50]  Abderrahim Benslimane,et al.  Learning in the Air: Secure Federated Learning for UAV-Assisted Crowdsensing , 2021, IEEE Transactions on Network Science and Engineering.

[51]  Jun Zhao,et al.  Privacy-Preserving Blockchain-Based Federated Learning for IoT Devices , 2019, IEEE Internet of Things Journal.

[52]  Khaled Salah,et al.  Towards Blockchain-Based Reputation-Aware Federated Learning , 2020, IEEE INFOCOM 2020 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).

[53]  Yipeng Zhou,et al.  CREAT: Blockchain-Assisted Compression Algorithm of Federated Learning for Content Caching in Edge Computing , 2022, IEEE Internet of Things Journal.

[54]  Xiwei Xu,et al.  Adaptable Blockchain-Based Systems: A Case Study for Product Traceability , 2017, IEEE Software.

[55]  Peter Richtárik,et al.  Federated Learning: Strategies for Improving Communication Efficiency , 2016, ArXiv.

[56]  Noor Zaman,et al.  Lightweight Authenticated-Encryption Scheme for Internet of Things Based on Publish-Subscribe Communication , 2020, IEEE Access.

[57]  Mohsen Guizani,et al.  Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications , 2015, IEEE Communications Surveys & Tutorials.

[58]  Axel Legay,et al.  Secure Architectures Implementing Trusted Coalitions for Blockchained Distributed Learning (TCLearn) , 2019, IEEE Access.

[59]  Pubudu N. Pathirana,et al.  Blockchain for Secure EHRs Sharing of Mobile Cloud Based E-Health Systems , 2019, IEEE Access.

[60]  Dusit Niyato,et al.  Auction Mechanisms in Cloud/Fog Computing Resource Allocation for Public Blockchain Networks , 2018, IEEE Transactions on Parallel and Distributed Systems.

[61]  Geyong Min,et al.  Federated Learning Based Proactive Content Caching in Edge Computing , 2018, 2018 IEEE Global Communications Conference (GLOBECOM).

[62]  Shashi Raj Pandey,et al.  A Crowdsourcing Framework for On-Device Federated Learning , 2019, IEEE Transactions on Wireless Communications.

[63]  Yan Zhang,et al.  Communication-Efficient Federated Learning for Digital Twin Edge Networks in Industrial IoT , 2022 .

[64]  Dusit Niyato,et al.  Resource Allocation in Mobility-Aware Federated Learning Networks: A Deep Reinforcement Learning Approach , 2019, 2020 IEEE 6th World Forum on Internet of Things (WF-IoT).

[65]  Wei Zhou,et al.  Delegated Proof of Stake With Downgrade: A Secure and Efficient Blockchain Consensus Algorithm With Downgrade Mechanism , 2019, IEEE Access.

[66]  Kannan Ramchandran,et al.  Robust Federated Learning in a Heterogeneous Environment , 2019, ArXiv.

[67]  Victor C. M. Leung,et al.  Distributed Resource Allocation in Blockchain-Based Video Streaming Systems With Mobile Edge Computing , 2019, IEEE Transactions on Wireless Communications.

[68]  Andreas Polze,et al.  Tangle Ledger for Decentralized Learning , 2020, 2020 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW).

[69]  H. Vincent Poor,et al.  Federated Learning With Differential Privacy: Algorithms and Performance Analysis , 2019, IEEE Transactions on Information Forensics and Security.

[70]  Howard H. Yang,et al.  Federated-Learning-Enabled Intelligent Fog Radio Access Networks: Fundamental Theory, Key Techniques, and Future Trends , 2020, IEEE Wireless Communications.

[71]  Zhou Su,et al.  BSIS: Blockchain-Based Secure Incentive Scheme for Energy Delivery in Vehicular Energy Network , 2019, IEEE Transactions on Industrial Informatics.

[72]  Aruna Seneviratne,et al.  Integration of Blockchain and Cloud of Things: Architecture, Applications and Challenges , 2019, IEEE Communications Surveys & Tutorials.

[73]  Kentaroh Toyoda,et al.  Mechanism Design for An Incentive-aware Blockchain-enabled Federated Learning Platform , 2019, 2019 IEEE International Conference on Big Data (Big Data).

[74]  Tianjian Chen,et al.  Federated Machine Learning: Concept and Applications , 2019 .

[75]  Jung Yeon Hwang,et al.  Efficient Privacy-Preserving Machine Learning for Blockchain Network , 2019, IEEE Access.

[76]  D. Niyato,et al.  Deep Reinforcement Learning Based Massive Access Management for Ultra-Reliable Low-Latency Communications , 2020, IEEE Transactions on Wireless Communications.

[77]  Yong Xiang,et al.  Decentralized Privacy Using Blockchain-Enabled Federated Learning in Fog Computing , 2020, IEEE Internet of Things Journal.

[78]  Dong In Kim,et al.  Resource Management for Blockchain-enabled Federated Learning: A Deep Reinforcement Learning Approach , 2020, ArXiv.

[79]  Walid Saad,et al.  Distributed Federated Learning for Ultra-Reliable Low-Latency Vehicular Communications , 2018, IEEE Transactions on Communications.

[80]  Wei Yu,et al.  Zero Knowledge Clustering Based Adversarial Mitigation in Heterogeneous Federated Learning , 2021, IEEE Transactions on Network Science and Engineering.

[81]  Yan Zhang,et al.  Blockchain and Federated Learning for Privacy-Preserved Data Sharing in Industrial IoT , 2020, IEEE Transactions on Industrial Informatics.

[82]  Xin Qin,et al.  FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare , 2019, IEEE Intelligent Systems.

[83]  Abdelhakim Senhaji Hafid,et al.  Record and Reward Federated Learning Contributions with Blockchain , 2019, 2019 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC).

[84]  Kin K. Leung,et al.  Adaptive Federated Learning in Resource Constrained Edge Computing Systems , 2018, IEEE Journal on Selected Areas in Communications.

[85]  Takayuki Nishio,et al.  Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge , 2018, ICC 2019 - 2019 IEEE International Conference on Communications (ICC).

[86]  Zhiguo Ding,et al.  A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art , 2019, IEEE Access.

[87]  Christopher Briggs,et al.  A Review of Privacy Preserving Federated Learning for Private IoT Analytics , 2020, ArXiv.

[88]  Wei Wang,et al.  CMFL: Mitigating Communication Overhead for Federated Learning , 2019, 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS).

[89]  Geyong Min,et al.  Communication-Efficient Federated Learning for Wireless Edge Intelligence in IoT , 2020, IEEE Internet of Things Journal.

[90]  Fei-Yue Wang,et al.  Economic Issues in Bitcoin Mining and Blockchain Research , 2018, 2018 IEEE Intelligent Vehicles Symposium (IV).

[91]  Paulo Silva,et al.  Impact of Geo-Distribution and Mining Pools on Blockchains: A Study of Ethereum - Practical Experience Report and Ongoing PhD Work , 2020, 2020 50th Annual IEEE-IFIP International Conference on Dependable Systems and Networks-Supplemental Volume (DSN-S).

[92]  Shui Yu,et al.  GAN Enhanced Membership Inference: A Passive Local Attack in Federated Learning , 2020, ICC 2020 - 2020 IEEE International Conference on Communications (ICC).

[93]  Wenchao Huang,et al.  FLChain: A Blockchain for Auditable Federated Learning with Trust and Incentive , 2019, 2019 5th International Conference on Big Data Computing and Communications (BIGCOM).

[94]  Ke Zhou,et al.  A low cost and un-cancelled laplace noise based differential privacy algorithm for spatial decompositions , 2020, World Wide Web.

[95]  Shijie Zhang,et al.  Mitigations on Sybil-based Double-spend Attacks in Bitcoin , 2020 .

[96]  Zhisheng Niu,et al.  Device Scheduling with Fast Convergence for Wireless Federated Learning , 2019, ICC 2020 - 2020 IEEE International Conference on Communications (ICC).

[97]  Albert Y. Zomaya,et al.  Federated Learning over Wireless Networks: Optimization Model Design and Analysis , 2019, IEEE INFOCOM 2019 - IEEE Conference on Computer Communications.