FedSelect-ME: A Secure Multi-Edge Federated Learning Framework with Adaptive Client Scoring

TL;DR

FedSelect-ME introduces a hierarchical multi-edge federated learning framework that improves scalability, security, and efficiency for privacy-sensitive healthcare data by utilizing adaptive client scoring and secure aggregation techniques.

Contribution

It presents a novel multi-edge FL architecture with adaptive client scoring and enhanced privacy protections, addressing scalability and security issues in traditional federated learning.

Findings

Achieves higher prediction accuracy on healthcare data

Reduces communication overhead compared to existing methods

Improves fairness across different regions

Abstract

Federated Learning (FL) enables collaborative model training without sharing raw data but suffers from limited scalability, high communication costs, and privacy risks due to its centralized architecture. This paper proposes FedSelect-ME, a hierarchical multi-edge FL framework that enhances scalability, security, and energy efficiency. Multiple edge servers distribute workloads and perform score-based client selection, prioritizing participants based on utility, energy efficiency, and data sensitivity. Secure Aggregation with Homomorphic Encryption and Differential Privacy protects model updates from exposure and manipulation. Evaluated on the eICU healthcare dataset, FedSelect-ME achieves higher prediction accuracy, improved fairness across regions, and reduced communication overhead compared to FedAvg, FedProx, and FedSelect. The results demonstrate that the proposed framework effectively addresses the bottlenecks of conventional FL, offering a secure, scalable, and efficient solution for large-scale, privacy-sensitive healthcare applications.