Research Paper#Federated Learning, Wireless Networks, Data Heterogeneity🔬 ResearchAnalyzed: Jan 3, 2026 15:41
Data Heterogeneity-Aware Client Selection for Federated Learning
Published:Dec 30, 2025 15:21
•1 min read
•ArXiv
Analysis
This paper addresses a critical challenge in Federated Learning (FL): data heterogeneity among clients in wireless networks. It provides a theoretical analysis of how this heterogeneity impacts model generalization, leading to inefficiencies. The proposed solution, a joint client selection and resource allocation (CSRA) approach, aims to mitigate these issues by optimizing for reduced latency, energy consumption, and improved accuracy. The paper's significance lies in its focus on practical constraints of FL in wireless environments and its development of a concrete solution to address data heterogeneity.
Key Takeaways
- •Addresses the problem of data heterogeneity in Federated Learning within wireless networks.
- •Provides a theoretical analysis of the impact of data heterogeneity on model generalization error.
- •Proposes a joint client selection and resource allocation (CSRA) approach to optimize for latency, energy consumption, and accuracy.
- •Demonstrates improved performance compared to baseline methods through simulations.
Reference
“The paper proposes a joint client selection and resource allocation (CSRA) approach, employing a series of convex optimization and relaxation techniques.”