Local Model Update for Blockchain Enabled Federated Learning: Approach and Analysis

Abstract

Federated learning (FL) has been considered as a promising distributed learning tool in massive data mining for different local devices. Addressing in the trust risk of centralized model aggregation and the challenge of data heterogeneity in traditional FL, this paper proposes an enhancement FL approach in a blockchain network. By analyzing the shortcakes of the classic FL that is widely used in the blockchain enabled FL networks, we propose a novel local parameter update approach, where the information of the last-round global model is utilized to reduce the local performance drift caused by data heterogeneity. The convergence of the proposed FL approach is then proved and the convergence rate is revealed to be linear to the training time. Finally, extensive experiments are carried out with a public dataset to validate the effectiveness of the proposed approach with comparisons of two classic baseline approaches.