Joint Communication Topology Reconstruction and Parameter Filtering for Heterogeneous Decentralized Federated Learning

Abstract

Federated learning (FL) has been strongly promoted in wireless edge networks because it facilitates collaborative training of machine learning models while ensuring the privacy and security of individual user data. Among different FL frameworks, Decentralized Federated Learning (DFL) framework offers greater flexibility and broader applicability compared to traditional Centralized Federated Learning (CFL) framework. However, existing DFL framework fails to accommodate the heterogeneity of multi-dimensional resources (e.g., communication and computing resources), and further suffer from high communication overhead. These limitations result in reduced training efficiency. To address these issues, this paper proposes SoloDFL, a framework integrating heterogeneity-aware communication topology reconstruction and parameter filtering. In SoloDFL, each client communicates with only one neighboring client, constructing a one-on-one communication topology, and exchanges only filtered local model parameters during communication. By doing so, SoloDFL copes with the synchronization barriers caused by system heterogeneity and further reduces communication overhead. The convergence of SoloDFL is proved theoretically. Extensive experiments show that SoloDFL achieves up to a $3.9\times $ acceleration and reduces communication costs by an average of 20.5% compared to the benchmark algorithms.