GFL: A Decentralized Federated Learning Framework Based On Blockchain

by   Yifan Hu, et al.

Due to people's emerging concern about data privacy, federated learning(FL) is currently being widely used. Conventional federated learning uses a highly centralized architecture, but in a real federated learning scenario, due to the highly distributed of data nodes and the existence of malicious data nodes, It is of great challenges for conventional federated learning to improve the utilization of network bandwidth and maintained the security and robustness of federated learning under malicious node attacks. In this paper, we propose an innovative Ring decentralized federated learning algorithm(RDFL) that not only makes full use of the bandwidth of the network but also improves the security and robustness of federated learning under malicious node attacks. At the same time, we encapsulated RDFL into a blochain-based federated learning framework called Galaxy Federated Learning framework<cit.> and used real data to perform experiments on the GFL to verify the effectiveness of the GFL.



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