Mobility Boosts Decentralized Federated Learning

This paper addresses a critical challenge in Decentralized Federated Learning (DFL): limited connectivity and data heterogeneity. It cleverly leverages user mobility, a characteristic of modern wireless networks, to improve information flow and overall DFL performance. The theoretical analysis and data-driven approach are promising, offering a practical solution to a real-world problem.

• •DFL performance is often limited by connectivity and data heterogeneity.
• •User mobility can enhance information flow in DFL.
• •The paper provides a theoretical analysis of mobility's impact on DFL convergence.
• •A data-driven DFL framework is proposed that utilizes mobile users with induced mobility patterns.
• •Experiments validate the approach and analyze the influence of network parameters.