Privacy-Preserving Byzantine-Robust Federated Learning via Blockchain Systems

Federated learning enables clients to train a machine learning model jointly without sharing their local data. However, due to the centrality of federated learning framework and the untrustworthiness of clients, traditional federated learning solutions are vulnerable to poisoning attacks from malicious clients and servers. In this paper, we aim to mitigate the impact of the central server and malicious clients by designing a Privacy-preserving Byzantine-robust Federated Learning (PBFL) scheme based on blockchain. Specifically, we use cosine similarity to judge the malicious gradients uploaded by malicious clients. Then, we adopt fully homomorphic encryption to provide secure aggregation. Finally, we use…

• NN
  National Natural Science Foundation of China

  Awards: 62125205, 62072361
• FR
  Fundamental Research Funds for the Central Universities

  Award: JB211505