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Abstract
The demand for intelligent industries and smart services based on big data is rising rapidly with the increasing digitization and intelligence of the modern world. This survey comprehensively reviews Blockchained Federated Learning (BlockFL) that joins the benefits of both Blockchain and Federated Learning to provide a secure and efficient solution for the demand. We compare the existing BlockFL models in four Internet-of-Things (IoT) application scenarios: Personal IoT (PIoT), Industrial IoT (IIoT), Internet of Vehicles (IoV), and Internet of Health Things (IoHT), with a focus on security and privacy, trust and reliability, efficiency, and data diversity. Our analysis shows that the features of decentralization and transparency make BlockFL a secure and effective solution for distributed model training, while the overhead and compatibility still need further study. It also reveals the unique challenges of each domain presents unique challenges, e.g., the requirement of accommodating dynamic environments in IoV and the high demands of identity and permission management in IoHT, in addition to some common challenges identified, such as privacy, resource constraints, and data heterogeneity. Furthermore, we examine the existing technologies that can benefit BlockFL, thereby helping researchers and practitioners to make informed decisions about the selection and development of BlockFL for various IoT application scenarios.
- Satyabrata Aich, Nday Kabulo Sinai, et al. 2021. Protecting Personal Healthcare Record Using Blockchain & Federated Learning Technologies. In 2021 23rd International Conference on Advanced Communication Technology (ICACT). IEEE, 109–112.Google Scholar
- Ala Al-Fuqaha, Mohsen Guizani, Mehdi Mohammadi, Mohammed Aledhari, and Moussa Ayyash. 2015. Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications. IEEE Communications Surveys & Tutorials 17, 4 (2015), 2347–2376.Google ScholarDigital Library
- Yasser Alharbi. 2022. A Novel Federated Learning based Lightweight Sustainable IoT Approach to Identify Abnormal Traffic. International Journal of Pervasive Computing and Communications (2022).Google ScholarCross Ref
- Aitizaz Ali, Bander Ali Saleh Al-Rimy, Ting Tin Tin, et al. 2023. Empowering Precision Medicine: Unlocking Revolutionary Insights through Blockchain-enabled Federated Learning and Electronic Medical Records. Sensors 23, 17 (2023), 7476.Google ScholarCross Ref
- 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(2021), 102355.Google Scholar
- Muhammad Salek Ali, Massimo Vecchio, Miguel Pincheira, Koustabh Dolui, Fabio Antonelli, and Mubashir Husain Rehmani. 2018. Applications of Blockchains in the Internet of Things: A Comprehensive Survey. IEEE Communications Surveys & Tutorials 21, 2 (2018), 1676–1717.Google ScholarCross Ref
- Omar Ali, Ashraf Jaradat, Atik Kulakli, and Ahmed Abuhalimeh. 2021. A Comparative Study: Blockchain Technology Utilization Benefits, Challenges and Functionalities. IEEE Access 9(2021), 12730–12749.Google ScholarCross Ref
- Yoshinori Aono, Takuya Hayashi, Lihua Wang, Shiho Moriai, et al. 2017. Privacy-preserving Deep Learning via Additively Homomorphic Encryption. IEEE Transactions on Information Forensics and Security 13, 5(2017), 1333–1345.Google Scholar
- Hany F Atlam and Gary B Wills. 2020. IoT Security, Privacy, Safety and Ethics. Digital twin technologies and smart cities(2020), 123–149.Google Scholar
- Sana Awan, Fengjun Li, Bo Luo, and Mei Liu. 2019. Poster: A Reliable and Accountable Privacy-preserving Federated Learning Framework using the Blockchain. In Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security. 2561–2563.Google ScholarDigital Library
- Xianglin Bao, Cheng Su, Yan Xiong, Wenchao Huang, and Yifei Hu. 2019. Flchain: A Blockchain for Auditable Federated Learning with Trust and Incentive. In 2019 5th International Conference on Big Data Computing and Communications (BIGCOM). IEEE, 151–159.Google Scholar
- Arjun Nitin Bhagoji, Supriyo Chakraborty, Prateek Mittal, and Seraphin Calo. 2019. Analyzing Federated Learning through an Adversarial Lens. In International Conference on Machine Learning. PMLR, 634–643.Google Scholar
- Franziska Boenisch, Adam Dziedzic, Roei Schuster, Ali Shahin Shamsabadi, Ilia Shumailov, and Nicolas Papernot. 2023. When the Curious Abandon Honesty: Federated Learning is not Private. In 2023 IEEE 8th European Symposium on Security and Privacy (EuroS&P). IEEE, 175–199.Google ScholarCross Ref
- Jorge Castillo, Phillip Rieger, Hossein Fereidooni, Qian Chen, and Ahmad Sadeghi. 2023. Fledge: Ledger-based Federated Learning Resilient to Inference and Backdoor Attacks. In Proceedings of the 39th Annual Computer Security Applications Conference. 647–661.Google ScholarDigital Library
- Miguel Castro, Barbara Liskov, et al. 1999. Practical Byzantine Fault Tolerance. In OsDI, Vol. 99. 173–186.Google Scholar
- 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 (2020).Google Scholar
- Hang Chen, Syed Ali Asif, Jihong Park, Chien-Chung Shen, and Mehdi Bennis. 2021. Robust Blockchained Federated Learning with Model Validation and Proof-of-stake Inspired Consensus. arXiv preprint arXiv:2101.03300(2021).Google Scholar
- Jin-Hua Chen, Min-Rong Chen, Guo-Qiang Zeng, and Jia-Si Weng. 2021. BDFL: A Byzantine-Fault-Tolerance Decentralized Federated Learning Method for Autonomous Vehicle. IEEE Transactions on Vehicular Technology 70, 9 (2021), 8639–8652.Google ScholarCross Ref
- Qiuling Chen, Ayong Ye, Qiang Zhang, and Chuan Huang. 2023. A New Edge Perturbation Mechanism for Privacy-preserving Data Collection in IoT. Chinese Journal of Electronics 32, 3 (2023), 1–10.Google ScholarCross Ref
- Wanshi Chen, Xingqin Lin, Juho Lee, Antti Toskala, Shu Sun, Carla Fabiana Chiasserini, and Lingjia Liu. 2023. 5G-Advanced Toward 6G: Past, Present, and Future. IEEE Journal on Selected Areas in Communications 41, 6(2023), 1592–1619.Google ScholarDigital Library
- JiuJun Cheng, JunLu Cheng, MengChu Zhou, FuQiang Liu, ShangCe Gao, and Cong Liu. 2015. Routing in Internet of Vehicles: A Review. IEEE Transactions on Intelligent Transportation Systems 16, 5(2015), 2339–2352.Google ScholarDigital Library
- Marco Conoscenti, Antonio Vetro, and Juan Carlos De Martin. 2016. Blockchain for the Internet of Things: A Systematic Literature Review. In 2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA). IEEE, 1–6.Google Scholar
- Jonathan Cook, Sabih Ur Rehman, and M Arif Khan. 2023. Security and Privacy for Low Power IoT Devices on 5G and Beyond Networks: Challenges and Future Directions. IEEE Access (2023).Google Scholar
- Harsh Bimal Desai, Mustafa Safa Ozdayi, and Murat Kantarcioglu. 2021. Blockfla: Accountable Federated Learning via Hybrid Blockchain Architecture. In Proceedings of the Eleventh ACM Conference on Data and Application Security and Privacy. 101–112.Google ScholarDigital Library
- Ali Dorri, Salil S Kanhere, and Raja Jurdak. 2017. Towards an Optimized Blockchain for IoT. In Proceedings of the second international conference on Internet-of-Things design and implementation. 173–178.Google ScholarDigital Library
- Omar El Rifai, Maelle Biotteau, Xavier de Boissezon, Imen Megdiche, Franck Ravat, and Olivier Teste. 2020. Blockchain-based Federated Learning in Medicine. In International Conference on Artificial Intelligence in Medicine. Springer, 214–224.Google Scholar
- Lei Feng, Yiqi Zhao, Shaoyong Guo, Xuesong Qiu, Wenjing Li, and Peng Yu. 2021. Blockchain-based Asynchronous Federated Learning for Internet of Things. IEEE Trans. Comput. (2021).Google Scholar
- Luca Franceschini and Alberto Midali. 2020. Industrial IoT: A Cost-benefit Analysis of Predictive Maintenance Service. (2020).Google Scholar
- Yuchuan Fu, Changle Li, F Richard Yu, Tom H Luan, and Pincan Zhao. 2023. An Incentive Mechanism of Incorporating Supervision Game for Federated Learning in Autonomous Driving. IEEE Transactions on Intelligent Transportation Systems (2023).Google ScholarDigital Library
- Liang Gao, Li Li, Yingwen Chen, ChengZhong Xu, and Ming Xu. 2022. FGFL: A Blockchain-based Fair Incentive Governor for Federated Learning. J. Parallel and Distrib. Comput. 163 (2022), 283–299.Google ScholarDigital Library
- Robin C Geyer, Tassilo Klein, and Moin Nabi. 2017. Differentially Private Federated Learning: A Client Level Perspective. arXiv preprint arXiv:1712.07557(2017).Google Scholar
- Vinay Gugueoth, Sunitha Safavat, Sachin Shetty, and Danda Rawat. 2023. A Review of IoT Security and Privacy using Decentralized Blockchain Techniques. Computer Science Review 50 (2023), 100585.Google ScholarDigital Library
- Hongzhi Guo, Xiaoyi Zhou, Jiajia Liu, and Yanning Zhang. 2022. Vehicular Intelligence in 6G: Networking, Communications, and Computing. Vehicular Communications 33 (2022), 100399.Google ScholarDigital Library
- Ankur Gupta, Purnendu Prabhat, and Bisma Gulzar. 2022. Personal-Internet-of-Things (PIoT): A Vision for Hyper-personalization Delivered Securely. In 2022 IEEE Delhi Section Conference (DELCON). IEEE, 1–6.Google ScholarCross Ref
- Malka N Halgamuge. 2022. Estimation of the Success Probability of a Malicious Attacker on Blockchain-based Edge Network. Computer Networks 219(2022), 109402.Google ScholarDigital Library
- Yufei Han and Xiangliang Zhang. 2020. Robust Federated Learning via Collaborative Machine Teaching. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 4075–4082.Google ScholarCross Ref
- Briland Hitaj, Giuseppe Ateniese, and Fernando Perez-Cruz. 2017. Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security. 603–618.Google ScholarDigital Library
- Shuyan Hu, Xiaojing Chen, Wei Ni, Ekram Hossain, and Xin Wang. 2021. Distributed Machine Learning for Wireless Communication Networks: Techniques, Architectures, and Applications. IEEE Communications Surveys & Tutorials 23, 3 (2021), 1458–1493.Google ScholarCross Ref
- 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.Google ScholarCross Ref
- Ahmed Imteaj, Urmish Thakker, Shiqiang Wang, Jian Li, and M Hadi Amini. 2021. A Survey on Federated Learning for Resource-constrained IoT Devices. IEEE Internet of Things Journal 9, 1 (2021), 1–24.Google ScholarCross Ref
- Anik Islam, Ahmed Al Amin, and Soo Young Shin. 2022. FBI: A Federated Learning-based Blockchain-Embedded Data Accumulation Scheme Using Drones for Internet of Things. IEEE Wireless Communications Letters 11, 5 (2022), 972–976. https://doi.org/10.1109/LWC.2022.3151873Google ScholarCross Ref
- Wael Issa, Nour Moustafa, Benjamin Turnbull, Nasrin Sohrabi, and Zahir Tari. 2023. Blockchain-based Federated Learning for Securing Internet of Things: A Comprehensive Survey. Comput. Surveys 55, 9 (2023), 1–43.Google ScholarDigital Library
- Abdul Rehman Javed, Muhammad Abul Hassan, Faisal Shahzad, Waqas Ahmed, Saurabh Singh, Thar Baker, and Thippa Reddy Gadekallu. 2022. Integration of Blockchain Technology and Federated Learning in Vehicular (IoT) Networks: A Comprehensive Survey. Sensors 22, 12 (2022), 4394.Google ScholarCross Ref
- Jiawen Kang, Zehui Xiong, et al. 2020. Scalable and Communication-efficient Decentralized Federated Edge Learning with Multi-blockchain Framework. In International Conference on Blockchain and Trustworthy Systems. Springer, 152–165.Google Scholar
- Jiawen Kang, Zehui Xiong, Xuandi Li, Yang Zhang, Dusit Niyato, Cyril Leung, and Chunyan Miao. 2021. Optimizing Task Assignment for Reliable Blockchain-Empowered Federated Edge Learning. IEEE Transactions on Vehicular Technology 70, 2 (2021), 1910–1923.Google ScholarCross Ref
- 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 6, 6 (2019), 10700–10714.Google ScholarCross Ref
- 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.Google ScholarCross Ref
- Jiawen Kang, Rong Yu, Xumin Huang, Sabita Maharjan, Yan Zhang, and Ekram Hossain. 2017. Enabling Localized Peer-to-peer Electricity Trading Among Plug-in Hybrid Electric Vehicles using Consortium Blockchains. IEEE Transactions on Industrial Informatics 13, 6 (2017), 3154–3164.Google ScholarCross Ref
- Shwet Ketu and Pramod Kumar Mishra. 2021. Internet of Healthcare Things: A Contemporary Survey. Journal of Network and Computer Applications 192 (2021), 103179.Google ScholarDigital Library
- Hyesung Kim, Jihong Park, et al. 2019. Blockchained On-device Federated Learning. IEEE Communications Letters 24, 6 (2019), 1279–1283.Google ScholarCross Ref
- You Jun Kim and Choong Seon Hong. 2019. Blockchain-based Node-aware Dynamic Weighting Methods for Improving Federated Learning Performance. In 2019 20th Asia-Pacific Network Operations and Management Symposium (APNOMS). IEEE, 1–4.Google Scholar
- Michael R Kosorok and Eric B Laber. 2019. Precision Medicine. Annual review of statistics and its application 6 (2019), 263–286.Google Scholar
- Kottilingam Kottursamy, Banupriya Sadayapillai, Ahmad Ali AlZubi, and Ali Kashif Bashir. 2023. A Novel Blockchain Architecture with Mutable Block and Immutable Transactions for Enhanced Scalability. Sustainable Energy Technologies and Assessments 58 (2023), 103320.Google ScholarCross Ref
- Rajesh Kumar, Abdullah Aman Khan, Jay Kumar, A Zakria, Noorbakhsh Amiri Golilarz, Simin Zhang, Yang Ting, Chengyu Zheng, and WenYong Wang. 2021. Blockchain-federated-learning and Deep Learning Models for COVID-19 Detection Using CT Imaging. IEEE Sensors Journal(2021).Google ScholarCross Ref
- Swaraj Kumar, Sandipan Dutta, Shaurya Chatturvedi, and MPS Bhatia. 2020. Strategies for Enhancing Training and Privacy in Blockchain enabled Federated Learning. In 2020 IEEE Sixth International Conference on Multimedia Big Data (BigMM). IEEE, 333–340.Google ScholarCross Ref
- Abdullah Lakhan, Mazin Abed Mohammed, et al. 2022. Federated-learning based Privacy Preservation and Fraud-enabled Blockchain IoMT System for Healthcare. IEEE journal of biomedical and health informatics 27, 2(2022), 664–672.Google Scholar
- Yixiao Lan, Yuan Liu, Boyang Li, and Chunyan Miao. 2021. Proof of Learning (PoLe): Empowering Machine Learning with Consensus Building on Blockchains. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35. 16063–16066.Google ScholarCross Ref
- Daoxing Li, Kai Xiao, Xiaohui Wang, Pengtian Guo, and Yong Chen. 2023. Towards Sparse Matrix Operations: Graph Database Approach for Power Grid Computation. Global Energy Interconnection 6, 1 (2023), 50–63.Google ScholarCross Ref
- Jun Li, Yumeng Shao, et al. 2021. Blockchain Assisted Decentralized Federated Learning (BLADE-FL): Performance Analysis and Resource Allocation. IEEE Transactions on Parallel and Distributed Systems 33, 10 (2021), 2401–2415.Google ScholarCross Ref
- Tian Li, Anit Kumar Sahu, Ameet Talwalkar, and Virginia Smith. 2020. Federated Learning: Challenges, Methods, and Future Directions. IEEE Signal Processing Magazine 37, 3 (2020), 50–60.Google ScholarCross Ref
- Xiang Li, Kaixuan Huang, et al. 2019. On the Convergence of Fedavg on Non-iid Data. arXiv preprint arXiv:1907.02189(2019).Google Scholar
- Zhuotao Lian, Weizheng Wang, Zhaoyang Han, and Chunhua Su. 2023. Blockchain-based Personalized Federated Learning for Internet of Medical Things. IEEE Transactions on Sustainable Computing(2023).Google ScholarCross Ref
- Chun-Cheng Lin, Ching-Tsorng Tsai, Yu-Liang Liu, Tsai-Ting Chang, and Yung-Sheng Chang. 2023. Security and Privacy in 5G-IIoT Smart Factories: Novel Approaches, Trends, and Challenges. Mobile Networks and Applications(2023), 1–16.Google Scholar
- 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(2021).Google ScholarCross Ref
- Wei Liu, Li Chen, and Wenyi Zhang. 2022. Decentralized Federated Learning: Balancing Communication and Computing Costs. IEEE Transactions on Signal and Information Processing over Networks 8 (2022), 131–143.Google ScholarCross Ref
- Yinghui Liu, Youyang Qu, Chenhao Xu, Zhicheng Hao, and Bruce Gu. 2021. Blockchain-enabled Asynchronous Federated Learning in Edge Computing. Sensors 21, 10 (2021), 3335.Google ScholarCross Ref
- Yuan Liu, Wangyuan Yu, Zhengpeng Ai, Guangxia Xu, Liang Zhao, and Zhihong Tian. 2022. A Blockchain-empowered Federated Learning in Healthcare-based Cyber Physical Systems. IEEE Transactions on Network Science and Engineering (2022).Google Scholar
- Sin Kit Lo, Yue Liu, Qinghua Lu, Chen Wang, Xiwei Xu, Hye-Young Paik, and Liming Zhu. 2022. Toward Trustworthy AI: Blockchain-based Architecture Design for Accountability and Fairness of Federated Learning Systems. IEEE Internet of Things Journal 10, 4 (2022), 3276–3284.Google ScholarCross Ref
- Xiaofeng Lu, Yuying Liao, Pietro Lio, and Pan Hui. 2020. Privacy-preserving Asynchronous Federated Learning Mechanism for Edge Network Computing. IEEE Access 8(2020), 48970–48981.Google ScholarCross Ref
- 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 16, 6 (2019), 4177–4186.Google ScholarCross Ref
- 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 69, 4 (2020), 4298–4311.Google ScholarCross Ref
- Shuaicheng Ma, Yang Cao, and Li Xiong. 2021. Transparent Contribution Evaluation for Secure Federated Learning on Blockchain. In 2021 IEEE 37th International Conference on Data Engineering Workshops (ICDEW). IEEE, 88–91.Google Scholar
- Umer Majeed and Choong Seon Hong. 2019. FLchain: Federated Learning via MEC-enabled Blockchain Network. In 2019 20th Asia-Pacific Network Operations and Management Symposium (APNOMS). IEEE, 1–4.Google ScholarCross Ref
- Imran Makhdoom, Mehran Abolhasan, Haider Abbas, and Wei Ni. 2019. Blockchain’s Adoption in IoT: The Challenges, and a Way Forward. Journal of Network and Computer Applications 125 (2019), 251–279.Google ScholarCross Ref
- Imran Makhdoom, Mehran Abolhasan, and Wei Ni. 2018. Blockchain for IoT: The Challenges and a Way Forward. In ICETE 2018-Proceedings of the 15th International Joint Conference on e-Business and Telecommunications.Google Scholar
- Imran Makhdoom, Ian Zhou, Mehran Abolhasan, Justin Lipman, and Wei Ni. 2019. PrivySharing: A Blockchain-based Framework for Integrity and Privacy-preserving Data Sharing in Smart Cities.. In ICETE (2). 363–371.Google Scholar
- Moustafa Mamdouh, Ali Ismail Awad, Hesham FA Hamed, and Ashraf AM Khalaf. 2020. Outlook on Security and Privacy in IoHT: Key Challenges and Future Vision. In Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020). Springer, 721–730.Google ScholarCross Ref
- Soujanya Mantravadi, Reto Schnyder, Charles Møller, and Thomas Ditlev Brunoe. 2020. Securing IT/OT Links for Low Power IIoT Devices: Design Considerations for Industry 4.0. IEEE Access 8(2020), 200305–200321.Google Scholar
- Ismael Martinez, Sreya Francis, and Abdelhakim Senhaji Hafid. 2019. Record and Reward Federated Learning Contributions with Blockchain. In 2019 International Conference on Cyber-enabled Distributed Computing and Knowledge Discovery (CyberC). IEEE, 50–57.Google Scholar
- Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. 2017. Communication-efficient Learning of Deep Networks from Decentralized Data. In Artificial Intelligence and Statistics. PMLR, 1273–1282.Google Scholar
- Ahmed Afif Monrat, Olov Schelén, and Karl Andersson. 2019. A Survey of Blockchain from the Perspectives of Applications, Challenges, and Opportunities. IEEE Access 7(2019), 117134–117151.Google ScholarCross Ref
- Wided Moulahi, Imen Jdey, Tarek Moulahi, Moatsum Alawida, and Abdulatif Alabdulatif. 2023. A Blockchain-based Federated Learning Mechanism for Privacy Preservation of Healthcare IoT Data. Computers in Biology and Medicine 167 (2023), 107630.Google ScholarDigital Library
- Tasiu Muazu, Mao Yingchi, Abdullahi Uwaisu Muhammad, Muhammad Ibrahim, Omaji Samuel, and Prayag Tiwari. 2023. IoMT: A Medical Resource Management System using Edge Empowered Blockchain Federated Learning. IEEE Transactions on Network and Service Management (2023).Google Scholar
- Salahadin Seid Musa, Marco Zennaro, Mulugeta Libsie, and Ermanno Pietrosemoli. 2022. Mobility-aware Proactive Edge Caching Optimization Scheme in Information-centric IoV Networks. Sensors 22, 4 (2022), 1387.Google ScholarCross Ref
- Mohamed Nahri, Azedine Boulmakoul, Lamia Karim, and Ahmed Lbath. 2018. IoV Distributed Architecture for Real-time Traffic Data Analytics. Procedia computer science 130 (2018), 480–487.Google Scholar
- Satoshi Nakamoto. 2008. Bitcoin: A Peer-to-peer Electronic Cash System. Decentralized Business Review(2008), 21260.Google Scholar
- Dinh C Nguyen, Peng Cheng, Ming Ding, David Lopez-Perez, Pubudu N Pathirana, Jun Li, Aruna Seneviratne, Yonghui Li, and H Vincent Poor. 2020. Enabling AI in Future Wireless Networks: A Data Life Cycle Perspective. IEEE Communications Surveys & Tutorials 23, 1 (2020), 553–595.Google ScholarCross Ref
- 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(2021).Google Scholar
- Takayuki Nishio and Ryo Yonetani. 2019. Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge. In ICC 2019-2019 IEEE International Conference on Communications (ICC). IEEE, 1–7.Google Scholar
- German I Parisi, Ronald Kemker, Jose L Part, Christopher Kanan, and Stefan Wermter. 2019. Continual Lifelong Learning with Neural Networks: A Review. Neural networks 113(2019), 54–71.Google Scholar
- Jonathan Passerat-Palmbach, Tyler Farnan, et al. 2020. Blockchain-orchestrated Machine Learning for Privacy Preserving Federated Learning in Electronic Health Data. In 2020 IEEE International Conference on Blockchain (Blockchain). IEEE, 550–555.Google Scholar
- 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 (2021).Google Scholar
- Shiva Raj Pokhrel. 2020. Federated Learning Meets Blockchain at 6G Edge: A Drone-assisted Networking for Disaster Response. In Proceedings of the 2nd ACM MobiCom Workshop on Drone Assisted Wireless Communications for 5G and Beyond. 49–54.Google ScholarDigital Library
- Shiva Raj Pokhrel and Jinho Choi. 2020. Federated Learning with Blockchain for Autonomous Vehicles: Analysis and Design Challenges. IEEE Transactions on Communications 68, 8 (2020), 4734–4746.Google ScholarCross Ref
- 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 58 (2021), 102748.Google ScholarCross Ref
- 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.Google ScholarCross Ref
- Attia Qammar, Ahmad Karim, Huansheng Ning, and Jianguo Ding. 2023. Securing Federated Learning with Blockchain: a Systematic Literature Review. Artificial Intelligence Review 56, 5 (2023), 3951–3985.Google ScholarDigital Library
- 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 7, 6 (2020), 5171–5183.Google ScholarCross Ref
- 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 17, 4 (2020), 2964–2973.Google ScholarCross Ref
- Youyang Qu, Md Palash Uddin, Chenquan Gan, Yong Xiang, Longxiang Gao, and John Yearwood. 2022. Blockchain-enabled Federated Learning: A Survey. Comput. Surveys 55, 4 (2022), 1–35.Google ScholarDigital Library
- 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.Google ScholarCross Ref
- Paritosh Ramanan and Kiyoshi Nakayama. 2020. Baffle: Blockchain based Aggregator Free Federated Learning. In 2020 IEEE International Conference on Blockchain (Blockchain). IEEE, 72–81.Google ScholarCross Ref
- Amirhossein Reisizadeh, Aryan Mokhtari, et al. 2020. Fedpaq: A Communication-efficient Federated Learning Method with Periodic Averaging and Quantization. In International Conference on Artificial Intelligence and Statistics. PMLR, 2021–2031.Google Scholar
- Joel JPC Rodrigues, Dante Borges De Rezende Segundo, Heres Arantes Junqueira, Murilo Henrique Sabino, Rafael Maciel Prince, Jalal Al-Muhtadi, and Victor Hugo C De Albuquerque. 2018. Enabling Technologies for the Internet of Health Things. Ieee Access 6(2018), 13129–13141.Google ScholarCross Ref
- Jihyeon Ryu, Dongho Won, and Youngsook Lee. 2021. A Study of Split Learning Model to Protect Privacy. Convergence Security Journal 21, 3 (2021), 49–56.Google Scholar
- Adeeb Salh, Lukman Audah, et al. 2021. A Survey on Deep Learning for Ultra-reliable and Low-latency Communications Challenges on 6G Wireless Systems. IEEE Access 9(2021), 55098–55131.Google ScholarCross Ref
- Omaji Samuel, Akogwu Blessing Omojo, et al. 2022. IoMT: A COVID-19 Healthcare System Driven by Federated Learning and Blockchain. IEEE Journal of Biomedical and Health Informatics 27, 2(2022), 823–834.Google ScholarCross Ref
- Najam U Saqib, Saif UR Malik, et al. 2023. Preserving Privacy in the Internet of Vehicles (IoV): A Novel Group Leader-based Shadowing Scheme using Blockchain. IEEE Internet of Things Journal(2023).Google Scholar
- Deepti Saraswat, Ashwin Verma, et al. 2022. Blockchain-based Federated Learning in UAVs Beyond 5G Networks: A Solution Taxonomy and Future Directions. IEEE Access 10(2022), 33154–33182. https://doi.org/10.1109/ACCESS.2022.3161132Google ScholarCross Ref
- Jayasree Sengupta, Sushmita Ruj, and Sipra Das Bit. 2020. A Comprehensive Survey on Attacks, Security Issues and Blockchain Solutions for IoT and IIoT. Journal of Network and Computer Applications 149 (2020), 102481.Google ScholarDigital Library
- Byoungjin Seok, Jinseong Park, and Jong Hyuk Park. 2019. A Lightweight Hash-based Blockchain Architecture for Industrial IoT. Applied Sciences 9, 18 (2019), 3740.Google ScholarCross Ref
- Sreenivas Sudarshan Seshadri, David Rodriguez, et al. 2020. Iotcop: A Blockchain-based Monitoring Framework for Detection and Isolation of Malicious Devices in Internet-of-Things Systems. IEEE Internet of Things Journal 8, 5 (2020), 3346–3359.Google ScholarCross Ref
- Hossein Shafagh, Lukas Burkhalter, Anwar Hithnawi, and Simon Duquennoy. 2017. Towards Blockchain-based Auditable Storage and Sharing of IoT Data. In Proceedings of the 2017 on cloud computing security workshop. 45–50.Google ScholarDigital Library
- Syed Waqas Haider Shah, Adnan Noor Mian, Adnan Aijaz, Junaid Qadir, and Jon Crowcroft. 2021. Energy-efficient MAC for Cellular IoT: State-of-the-art, Challenges, and Standardization. IEEE Transactions on Green Communications and Networking 5, 2 (2021), 587–599.Google ScholarCross Ref
- 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 8, 4 (2020), 2265–2275.Google ScholarCross Ref
- Andrew Ronald Short, Helen C Leligou, Michael Papoutsidakis, and Efstathios Theocharis. 2020. Using Blockchain Technologies to Improve Security in Federated Learning Systems. In 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC). IEEE, 1183–1188.Google Scholar
- 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 129 (2022), 380–388.Google ScholarDigital Library
- Emiliano Sisinni, Abusayeed Saifullah, Song Han, Ulf Jennehag, and Mikael Gidlund. 2018. Industrial Internet of Things: Challenges, Opportunities, and Directions. IEEE Transactions on Industrial Informatics 14, 11 (2018), 4724–4734.Google ScholarCross Ref
- Ekta Soni and Khyati Chopra. 2023. IoHT: Healthcare with the Internet of Things. In IoT and Cloud Computing-based Healthcare Information Systems. Apple Academic Press, 65–82.Google Scholar
- Yuwei Sun, Hiroshi Esaki, and Hideya Ochiai. 2020. Blockchain-based Federated Learning Against End-Point Adversarial Data Corruption. In 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 729–734.Google Scholar
- Zhe Sun, Jiyuan Feng, Lihua Yin, Zixu Zhang, Ran Li, Yu Hu, and Chongning Na. 2022. Fed-DFE: A Decentralized Function Encryption-based Privacy-preserving Scheme for Federated Learning. CMC-COMPUTERS MATERIALS & CONTINUA 71, 1 (2022), 1867–1886.Google ScholarCross Ref
- Nick Szabo. 1997. Formalizing and Securing Relationships on Public Networks. First Monday (1997).Google Scholar
- Mohammad Hossein Tabatabaei, Roman Vitenberg, and Narasimha Raghavan Veeraragavan. 2023. Understanding blockchain: Definitions, architecture, design, and system comparison. Computer Science Review 50 (2023), 100575.Google ScholarDigital Library
- Soo Fun Tan and Azman Samsudin. 2021. Recent Technologies, Security Countermeasure and Ongoing Challenges of Industrial Internet of Things (IIoT): A Survey. Sensors 21, 19 (2021), 6647.Google ScholarCross Ref
- Kentaroh Toyoda and Allan N Zhang. 2019. Mechanism Design for an Incentive-aware Blockchain-enabled Federated Learning Platform. In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 395–403.Google ScholarCross Ref
- Irshad Ullah, Xiaoheng Deng, Xinjun Pei, Husnain Mushtaq, and Muhammad Uzair. 2023. IoV-SFL: A Blockchain-based Federated Learning Framework for Secure and Efficient Data Sharing in the Internet of Vehicles. (2023).Google Scholar
- Muhammad Habib ur Rehman, Khaled Salah, Ernesto Damiani, and Davor Svetinovic. 2020. Towards Blockchain-based Reputation-aware Federated Learning. In IEEE INFOCOM 2020-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). IEEE, 183–188.Google Scholar
- 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.Google ScholarCross Ref
- Hongyi Wang, Mikhail Yurochkin, Yuekai Sun, Dimitris Papailiopoulos, and Yasaman Khazaeni. 2020. Federated Learning with Matched Averaging. arXiv preprint arXiv:2002.06440(2020).Google Scholar
- Naiyu Wang, Wenti Yang, Xiaodong Wang, Longfei Wu, Zhitao Guan, Xiaojiang Du, and Mohsen Guizani. 2022. A Blockchain based Privacy-preserving Federated Learning Scheme for Internet of Vehicles. Digital Communications and Networks(2022).Google Scholar
- Shufen Wang. 2019. BlockFedML: Blockchained Federated Machine Learning Systems. In 2019 International Conference on Intelligent Computing, Automation and Systems (ICICAS). IEEE, 751–756.Google Scholar
- Xu Wang, Wei Ni, Xuan Zha, Guangsheng Yu, Ren Ping Liu, Nektarios Georgalas, and Andrew Reeves. 2021. Capacity Analysis of Public Blockchain. Computer Communications 177 (2021), 112–124.Google ScholarDigital Library
- Xu Wang, Guangsheng Yu, Xuan Zha, Wei Ni, Ren Ping Liu, Y Jay Guo, Kangfeng Zheng, and Xinxin Niu. 2019. Capacity of Blockchain based Internet-of-Things: Testbed and Analysis. Internet of Things 8(2019), 100109.Google ScholarCross Ref
- Xu Wang, Ping Yu, Guangsheng Yu, et al. 2019. A High-performance Hybrid Blockchain System for Traceable IoT Applications. In Network and System Security: 13th International Conference, NSS 2019, Sapporo, Japan, December 15–18, 2019, Proceedings 13. Springer, 721–728.Google ScholarDigital Library
- Xu Wang, Xuan Zha, Guangsheng Yu, Wei Ni, and Ren Ping Liu. 2020. Blockchain for Internet of Things. In Blockchains for Network Security: Principles, Technologies and Applications. The Institution of Engineering and Technology.Google Scholar
- Yuntao Wang, Haixia Peng, Zhou Su, Tom H Luan, Abderrahim Benslimane, and Yuan Wu. 2022. A Platform-free Proof of Federated Learning Consensus Mechanism for Sustainable Blockchains. IEEE Journal on Selected Areas in Communications 40, 12(2022), 3305–3324.Google ScholarCross Ref
- Zhilin Wang, Qin Hu, Ruinian Li, Minghui Xu, and Zehui Xiong. 2023. Incentive Mechanism Design for Joint Resource Allocation in Blockchain-based Federated Learning. IEEE Transactions on Parallel and Distributed Systems 34, 5 (2023), 1536–1547.Google ScholarDigital Library
- Karl Weiss, Taghi M Khoshgoftaar, and DingDing Wang. 2016. A Survey of Transfer Learning. Journal of Big data 3, 1 (2016), 1–40.Google ScholarCross Ref
- 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 (2019).Google Scholar
- Leon Witt, Usama Zafar, et al. 2023. Decentralized and Incentivized Federated Learning: A Blockchain-enabled Framework Utilising Compressed Soft-Labels and Peer Consistency. IEEE Transactions on Services Computing(2023).Google Scholar
- Lang Wu, Weijian Ruan, Jinhui Hu, and Yaobin He. 2023. A Survey on Blockchain-based Federated Learning. Future Internet 15, 12 (2023), 400.Google ScholarCross Ref
- Yulei Wu, Hong-Ning Dai, and Hao Wang. 2020. Convergence of Blockchain and Edge Computing for Secure and Scalable IIoT Critical Infrastructures in Industry 4.0. IEEE Internet of Things Journal 8, 4 (2020), 2300–2317.Google ScholarCross Ref
- 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 15 (2019), 911–926.Google ScholarDigital Library
- Guangxia Xu, Zhaojian Zhou, Jingnan Dong, Lejun Zhang, and Xiaoling Song. 2023. A Blockchain-based Federated Learning Scheme for Data Sharing in Industrial Internet of Things. IEEE Internet of Things Journal(2023).Google ScholarCross Ref
- Yajing Xu, Zhihui Lu, Keke Gai, Qiang Duan, Junxiong Lin, Jie Wu, and Kim-Kwang Raymond Choo. 2021. Besifl: Blockchain Empowered Secure and Incentive Federated Learning Paradigm in IoT. IEEE Internet of Things Journal(2021).Google Scholar
- Shichang Xuan, Ming Jin, Xin Li, Zhaoyuan Yao, Wu Yang, and Dapeng Man. 2021. DAM-SE: A Blockchain-based Optimized Solution for the Counterattacks in the Internet of Federated Learning Systems. Security and Communication Networks 2021 (2021).Google Scholar
- Zhanpeng Yang, Yuanming Shi, Yong Zhou, Zixin Wang, and Kai Yang. 2022. Trustworthy Federated Learning via Blockchain. IEEE Internet of Things Journal 10, 1 (2022), 92–109.Google ScholarCross Ref
- Zhe Yang, Kan Yang, Lei Lei, Kan Zheng, and Victor CM Leung. 2018. Blockchain-based Decentralized Trust Management in Vehicular Networks. IEEE Internet of Things Journal 6, 2 (2018), 1495–1505.Google ScholarCross Ref
- Abbas Yazdinejad, Ali Dehghantanha, et al. 2022. Block Hunter: Federated Learning for Cyber Threat Hunting in Blockchain-based IIoT Networks. IEEE Transactions on Industrial Informatics 18, 11 (2022), 8356–8366.Google ScholarCross Ref
- Bo Yin, Hao Yin, Yulei Wu, and Zexun Jiang. 2020. FDC: A Secure Federated Deep Learning Mechanism for Data Collaborations in the Internet of Things. IEEE Internet of Things Journal 7, 7 (2020), 6348–6359.Google ScholarCross Ref
- Guangsheng Yu, Xu Wang, et al. 2020. Survey: Sharding in Blockchains. IEEE Access 8(2020), 14155–14181.Google ScholarCross Ref
- Guangsheng Yu, Xu Wang, Caijun Sun, Qin Wang, Ping Yu, Wei Ni, and Ren Ping Liu. 2023. Ironforge: An Open, Secure, Fair, Decentralized Federated Learning. IEEE Transactions on Neural Networks and Learning Systems (2023).Google Scholar
- Guangsheng Yu, Xu Wang, Kan Yu, Wei Ni, J Andrew Zhang, and Ren Ping Liu. 2020. Scaling-out Blockchains with Sharding: an Extensive Survey.Google Scholar
- Guangsheng Yu, Xu Wang, Ping Yu, Caijun Sun, Wei Ni, and Ren Ping Liu. 2022. Dataset Obfuscation: Its Applications to and Impacts on Edge Machine Learning. arXiv preprint arXiv:2208.03909(2022).Google Scholar
- Guangsheng Yu, Xuan Zha, et al. 2020. A Unified Analytical Model for Proof-of-X Schemes. Computers & Security 96(2020), 101934.Google ScholarCross Ref
- Guangsheng Yu, Xuan Zha, Xu Wang, Wei Ni, Kan Yu, Ping Yu, J Andrew Zhang, Ren Ping Liu, and Y Jay Guo. 2020. Enabling Attribute Revocation for Fine-grained Access Control in Blockchain-IoT Systems. IEEE Transactions on Engineering Management 67, 4 (2020), 1213–1230.Google ScholarCross Ref
- Guangsheng Yu, Litianyi Zhang, Xu Wang, Kan Yu, Wei Ni, J Andrew Zhang, and Ren Ping Liu. 2021. A Novel Dual-Blockchained Structure for Contract-theoretic LoRa-based Information Systems. Information Processing & Management 58, 3 (2021), 102492.Google ScholarDigital Library
- Shuo Yuan, Bin Cao, Mugen Peng, and Yaohua Sun. 2021. ChainsFL: Blockchain-driven Federated Learning from Design to Realization. In 2021 IEEE Wireless Communications and Networking Conference (WCNC). IEEE, 1–6.Google ScholarDigital Library
- Shuo Yuan, Bin Cao, Yao Sun, Zhiguo Wan, and Mugen Peng. 2024. Secure and Efficient Federated Learning through Layering and Sharding Blockchain. IEEE Transactions on Network Science and Engineering (2024).Google ScholarCross Ref
- Xin Yuan, Wei Ni, Ming Ding, Kang Wei, Jun Li, and H Vincent Poor. 2023. Amplitude-Varying Perturbation for Balancing Privacy and Utility in Federated Learning. IEEE Transactions on Information Forensics and Security (2023).Google ScholarDigital Library
- Umar Zaman, Imran, Faisal Mehmood, Naeem Iqbal, Jungsuk Kim, and Muhammad Ibrahim. 2022. Towards Secure and Intelligent Internet of Health Things: A Survey of Enabling Technologies and Applications. Electronics 11, 12 (2022), 1893.Google Scholar
- Xuan Zha, Xu Wang, Wei Ni, Ren Ping Liu, Y Jay Guo, Xinxin Niu, and Kangfeng Zheng. 2017. Analytic Model on Data Security in VANETs. In 2017 17th International Symposium on Communications and Information Technologies (ISCIT). IEEE, 1–6.Google Scholar
- Ke Zhang, Yongxu Zhu, Sabita Maharjan, and Yan Zhang. 2019. Edge Intelligence and Blockchain Empowered 5G beyond for the Industrial Internet of Things. IEEE Network 33, 5 (2019), 12–19.Google ScholarDigital Library
- 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 8, 7 (2020), 5926–5937.Google ScholarCross Ref
- Zixu Zhang, Xu Wang, et al. 2022. A Community Detection-based Blockchain Sharding Scheme. In Blockchain–ICBC 2022: 5th International Conference, Held as part of the Services Conference Federation, SCF 2022, Honolulu, HI, USA, December 10–14, 2022, Proceedings. Springer, 78–91.Google Scholar
- Lingchen Zhao, Qian Wang, Qin Zou, Yan Zhang, and Yanjiao Chen. 2019. Privacy-preserving Collaborative Deep Learning with Unreliable Participants. IEEE Transactions on Information Forensics and Security 15 (2019), 1486–1500.Google ScholarDigital Library
- 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 8, 3 (2020), 1817–1829.Google ScholarCross Ref
- Jingheng Zheng, Hui Tian, Wanli Ni, Wei Ni, and Ping Zhang. 2022. Balancing Accuracy and Integrity for Reconfigurable Intelligent Surface-aided Over-the-air Federated Learning. IEEE Transactions on Wireless Communications 21, 12(2022), 10964–10980.Google ScholarCross Ref
- Juncen Zhu, Jiannong Cao, Divya Saxena, Shan Jiang, and Houda Ferradi. 2023. Blockchain-empowered Federated Learning: Challenges, Solutions, and Future Directions. Comput. Surveys 55, 11 (2023), 1–31.Google ScholarDigital Library
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