Christos Sad

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

2025

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, …