DegaFL: Decentralized Gradient Aggregation for Cross-Silo Federated Learning
Jialiang Han, Yudong Han, Xiang Jing, Gang L. Huang, Yun Ma · IEEE Transactions on Parallel and Distributed Systems · 2024
Federated learning (FL) is an emerging promising paradigm of privacy-preserving machine learning (ML). An important type of FL is cross-silo FL, which enables a moderate number of organizations to cooperatively train a shared model by keeping confidential data locally and aggregating gradients on a central parameter server. However, the central server may be vulnerable to malicious attacks or software failures in practice. To address this issue, in this paper, we propose$\mathtt{DegaFL} $, a novel decentralized gradient aggregation approach for cross-silo FL.$\mathtt{DegaFL} $eliminates the central server by aggregating gradients on each participant, and maintains and synchronizes gradients of only the current training round. Besides, we propose$\mathtt{AdaAgg} $to adaptively aggregate correct gradients from honest nodes and use HotStuff to ensure the consistency of the training round number and gradients among all nodes. Experimental results show that$\mathtt{DegaFL} $defends against common threat models with minimal accuracy loss, and achieves up to$50\times$reduction in storage overhead and up to$13\times$reduction in network overhead, compared to state-of-the-art decentralized FL approaches.