A Blockchain-based federated learning framework for secure aggregation and fair incentives

References

• Alsamhi, S. H., Shvetsov, A. V., Hawbani, A., Shvetsova, S. V., Kumar, S., & Zhao, L. (2023). Survey on Federated Learning enabling indoor navigation for industry 4.0 in B5G. Future Generation Computer Systems, 148, 250–265. https://doi.org/10.1016/j.future.2023.06.001
   Web of Science ®Google Scholar
• Alsamhi, S. H., Almalki, F. A., Afghah, F., Hawbani, A., Shvetsov, A. V., Lee, B., & Song, H. (2022). Drones’ edge intelligence over smart environments in B5G: Blockchain and federated learning synergy. IEEE Transactions on Green Communications and Networking, 6(1), 295–312. https://doi.org/10.1109/TGCN.2021.3132561
   Google Scholar
• Ben Saad, S., Brik, B., & Ksentini, A. (2023). Toward securing federated learning against poisoning attacks in zero touch B5G networks. IEEE Transactions on Network and Service Management, 20(2), 1612–1624. https://doi.org/10.1109/TNSM.2023.3278838
   Web of Science ®Google Scholar
• Chen, H, Asif, S A, Park, J, Shen, C.-C, & Bennis, M. (2021). Robust Blockchained Federated Learning with Model Validation and Proof-of-Stake Inspired Consensus. arXiv preprint arXiv:2101.03300.
   Google Scholar
• Ferrag, M. A., & Shu, L. (2021). The performance evaluation of blockchain-based security and privacy systems for the internet of things: A tutorial. IEEE Internet of Things Journal, 8(24), 17236–17260. https://doi.org/10.1109/JIOT.2021.3078072
   Web of Science ®Google Scholar
• Gao, L., Li, L., Chen, Y., Xu, C., & Xu, M. (2022). FGFL: A blockchain-based fair incentive governor for Federated Learning. Journal of Parallel and Distributed Computing, 163, 283–299. https://doi.org/10.1016/j.jpdc.2022.01.019
   Web of Science ®Google Scholar
• Guo, W., Wang, Y., & Jiang, P. (2023). Incentive mechanism design for Federated Learning with Stackelberg game perspective in the industrial scenario. Computers & Industrial Engineering, 184, 109592. https://doi.org/10.1016/j.cie.2023.109592
   Web of Science ®Google Scholar
• Hasan, O., Brunie, L., & Bertino, E. (2023). Privacy-Preserving reputation systems based on blockchain and other cryptographic building blocks: A survey. ACM Computing Surveys, 55(2), 1–37. https://doi.org/10.1145/3490236
   Web of Science ®Google Scholar
• Huang, C., Zhao, Y., Chen, H., Wang, X., Zhang, Q., Chen, Y., Wang, H., & Lam, K.-Y. (2022). Zkrep: A privacy-preserving scheme for reputation-based blockchain system. IEEE Internet of Things Journal, 9(6), 4330–4342. https://doi.org/10.1109/JIOT.2021.3105273
   Web of Science ®Google Scholar
• Jiang, T., Shen, G., Guo, C., Cui, Y., & Xie, B. (2023). Bfls: Blockchain and federated learning for sharing threat detection models as cyber threat intelligence. Computer Networks, 224, 109604. https://doi.org/10.1016/j.comnet.2023.109604
   Web of Science ®Google Scholar
• Kang, J., Xiong, Z., Niyato, D., Zou, Y., Zhang, Y., & Guizani, M. (2020). Reliable federated learning for mobile networks. IEEE Wireless Communications, 27(2), 72–80. https://doi.org/10.1109/MWC.001.1900119
   Web of Science ®Google Scholar
• Kang, J., Xiong, Z., Niyato, D., Xie, S., & Zhang, J. (2019). Incentive Mechanism for Reliable Federated Learning: A Joint Optimization Approach to Combining Reputation and Contract Theory. IEEE Internet of Things Journal, 6(6), 10700–10714. http://dx.doi.org/10.1109/JIoT.6488907
   Web of Science ®Google Scholar
• Kim, H., Park, J., Bennis, M., & Kim, S.-L. (2020). Blockchained On-device federated learning. IEEE Communications Letters, 24(6), 1279–1283. https://doi.org/10.1109/LCOMM.2019.2921755
   Web of Science ®Google Scholar
• Li, Y., Chen, C., Liu, N., Huang, H., Zheng, Z., & Yan, Q. (2021). A blockchain-based decentralized federated learning framework with committee consensus. IEEE Network, 35(1), 234–241. https://doi.org/10.1109/MNET.011.2000263
   Web of Science ®Google Scholar
• Liao, Z., & Cheng, S. (2023). RVC: A reputation and voting based blockchain consensus mechanism for edge computingenabled IoT systems. Journal of Network and Computer Applications, 209, 103510. https://doi.org/10.1016/j.jnca.2022.103510
   Web of Science ®Google Scholar
• Liu, W., He, Y., Wang, X., Duan, Z., Liang, W., & Liu, Y. (2023). BFG: Privacy protection framework for internet of medical things based on blockchain and federated learning. Connection Science, 35(1), 2199951. https://doi.org/10.1080/09540091.2023.2199951
   Web of Science ®Google Scholar
• Madill, E., Nguyen, B., Leung, C. K., & Rouhani, S. (2022). Scalesfl: A Sharding Solution for Blockchain-Based Federated Learning. Proceedings of the Fourth ACM International Symposium on Blockchain and Secure Critical Infrastructure, 95–106. https://doi.org/10.1145/3494106.3528680.
   Google Scholar
• McMahan, B., Moore, E., Ramage, D., Hampson, S., & Arcas, B. A. (2017). Communication-efficient learning of deep networks from decentralized data. In Artificial intelligence and statistics (pp. 1273–1282). https://proceedings.mlr.press/v54/mcmahan17a.html.
   Google Scholar
• Myrzashova, R., Alsamhi, S. H., Shvetsov, A. V., Hawbani, A., & Wei, X. (2023). Blockchain meets federated learning in healthcare: A systematic review With challenges and opportunities. IEEE Internet of Things Journal, 10(16), 14418–14437. https://doi.org/10.1109/JIOT.2023.3263598
   Web of Science ®Google Scholar
• Oliveira, M. T. d., Reis, L. H. A., Medeiros, D. S. V., Carrano, R. C., Olabarriaga, S. D., & Mattos, D. M. F. (2020). Blockchain reputation-based consensus: A scalable and resilient mechanism for distributed mistrusting applications. Computer Networks, 179, 107367. https://doi.org/10.1016/j.comnet.2020.107367
   Web of Science ®Google Scholar
• Peng, Z., Xu, J., Chu, X., Gao, S., Yao, Y., Gu, R., & Tang, Y. (2022). Vfchain: Enabling verifiable and auditable federated learning via blockchain systems. IEEE Transactions on Network Science and Engineering, 9(1), 173–186. https://doi.org/10.1109/TNSE.2021.3050781
   Web of Science ®Google Scholar
• Qi, J., Lin, F., Chen, Z., Tang, C., Jia, R., & Li, M. (2022). High-Quality model aggregation for blockchain-based federated learning via reputation-motivated task participation. IEEE Internet of Things Journal, 9(19), 18378–18391. https://doi.org/10.1109/JIOT.2022.3160425
   Web of Science ®Google Scholar
• Qin, Z., Ye, J., Meng, J., Lu, B., & Wang, L. (2022). Privacy-Preserving blockchain-based federated learning for marine internet of things. IEEE Transactions on Computational Social Systems, 9(1), 159–173. https://doi.org/10.1109/TCSS.2021.3100258
   Web of Science ®Google Scholar
• Qu, Y., Gao, L., Xiang, Y., Shen, S., & Yu, S. (2022). Fedtwin: Blockchain-enabled adaptive asynchronous federated learning for digital twin networks. IEEE Network, 36(6), 183–190. https://doi.org/10.1109/MNET.105.2100620
   Web of Science ®Google Scholar
• Shayan, M., Fung, C., Yoon, C. J. M., & Beschastnikh, I. (2021). Biscotti: A blockchain system for private and secure federated learning. IEEE Transactions on Parallel and Distributed Systems, 32(7), 1513–1525. https://doi.org/10.1109/TPDS.2020.3044223
   Web of Science ®Google Scholar
• Singh, S., Rathore, S., Alfarraj, O., Tolba, A., & Yoon, B. (2022). A framework for privacy-preservation of IoT healthcare data using Federated Learning and blockchain technology. Future Generation Computer Systems, 129, 380–388. https://doi.org/10.1016/j.future.2021.11.028
   Web of Science ®Google Scholar
• Song, Z., Sun, H., Yang, H. H., Wang, X., Zhang, Y., & Quek, T. Q. S. (2022). Reputation-Based federated learning for secure wireless networks. IEEE Internet of Things Journal, 9(2), 1212–1226. https://doi.org/10.1109/JIOT.2021.3079104
   Web of Science ®Google Scholar
• Wang, Z., Hu, Q., Li, R., Xu, M., & Xiong, Z. (2022). Incentive Mechanism Design for Joint Resource Allocation in Blockchain-based Federated Learning (arXiv:2202.10938). arXiv. http://arxiv.org/abs/2202.10938.
   Google Scholar
• Xu, M., & Li, X. (2023). FedG2L: A privacy-preserving federated learning scheme base on “G2L” against poisoning attack. Connection Science, 35(1), 2197173. https://doi.org/10.1080/09540091.2023.2197173
   Web of Science ®Google Scholar
• Xu, R., & Chen, Y. (2022). Mdfl: A secure microchained decentralized federated learning fabric atop IoT networks. IEEE Transactions on Network and Service Management, 19(3), 2677–2688. https://doi.org/10.1109/TNSM.2022.3179892
   Web of Science ®Google Scholar
• Yu, H., Liu, Z., Liu, Y., Chen, T., Cong, M., Weng, X., Niyato, D., & Yang, Q. (2020). A fairness-aware incentive scheme for federated learning. Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, 393–399. https://doi.org/10.1145/3375627.3375840
   Google Scholar
• Zhang, K., Song, X., Zhang, C., & Yu, S. (2022). Challenges and future directions of secure federated learning: A survey. Frontiers of Computer Science, 16(5), 165817. https://doi.org/10.1007/s11704-021-0598-z
   PubMed Web of Science ®Google Scholar
• Zhang, B., Wang, X., Xie, R., Li, C., Zhang, H., & Jiang, F. (2023). A reputation mechanism based Deep Reinforcement Learning and blockchain to suppress selfish node attack motivation in Vehicular Ad-Hoc Network. Future Generation Computer Systems, 139, 17–28. https://doi.org/10.1016/j.future.2022.09.010
   Google Scholar
• Zhang, S., & Zhu, J. (2023). Privacy protection federated learning framework based on blockchain and committee consensus in IoT devices. 2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC), 627–636. https://doi.org/10.1109/COMPSAC57700.2023.00088
   Google Scholar
• Zhang, T., Gao, L., He, C., Zhang, M., Krishnamachari, B., & Avestimehr, S. (2022). Federated Learning for Internet of Things: Applications, Challenges, and Opportunities. (arXiv:2111.07494). arXiv. http://arxiv.org/abs/2111.07494.
   Google Scholar
• Zhu, J., Cao, J., Saxena, D., Jiang, S., & Ferradi, H. (2023). Blockchain-empowered federated learning: Challenges, solutions, and future directions. ACM Computing Surveys, 55(11), 1–31. https://doi.org/10.1145/3570953
   Web of Science ®Google Scholar