Towards Asynchronous Peer-to-Peer Federated Learning for Heterogeneous Systems

Federated Learning (FL) enables collaborative model training across distributed, privacy-sensitive data sources. Traditional FL follows a centralized client-server architecture, relying on synchronized updates and uniform participation. However, real-world deployments face challenges such as client heterogeneity, stragglers, non-independent data distributions, and single points of failure due to server centralization. To address these limitations, we propose an asynchronous Peer-to-Peer FL scheme that enhances learning efficiency in heterogeneous environments. Our method employs a gradient-aware aggregation algorithm with a progress-based adaptive fusion weight, mitigating the impact of resource disparities among clients. Experimental results on CIFAR-10/100 datasets indicate that our scheme achieves 4.8 -- 16.3% and 10.9 -- 37.7% higher accuracy compared to FedAVG and FedSGD, considering constrained total number of exchanged updates among clients. Furthermore, it effectively handles client heterogeneity through its dynamic fusion weight adjustment.