MChain-SFFL: Multi-Chain Aggregation Privacy Preserving for Server-Free Federated Learning

Federated Learning is a distributed learning paradigm that allows multiple organizations or devices to train a global model collaboratively in a privacy-preserving manner. However, there still exists privacy leakage risks due to the curious or dishonest server. In this paper, we propose a novel server-free federated learning paradigm named MChain-SFFL, which utilizes parallel multi-chain aggregation to mitigate privacy leakage risks and enhance convergence speed. First, MChain-SFFL randomly selects multiple users as chain heads. Then, MChain-SFFL utilizes parallel multi-chain communication mechanism to transmit the masked local model parameters. Finally, every chain head computes the model update for that chain and sends it to the other users. Upon receiving updates from all other chains, each user aggregates the received parameters to generate the model update for the current round. We validate the superiority of our method in accuracy and convergence speed on both image datasets and text datasets. Experimental results show that MChain-SFFL achieves superior privacy protection without impairing model accuracy and exhibits robustness to Non-IID data.