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

2025-10-29

AI Summary

To address the scalability limitations, high communication overhead, and elevated privacy risks inherent in conventional centralized federated learning (FL), this paper proposes a hierarchical multi-edge FL framework tailored for large-scale, privacy-sensitive healthcare applications. The method introduces: (1) an adaptive client scoring mechanism integrating utility, energy efficiency, and data sensitivity; (2) an end-edge collaborative privacy-preserving architecture combining homomorphic encryption, differential privacy, and secure aggregation; and (3) fair and efficient model aggregation across multiple edge servers. Experiments on the eICU dataset demonstrate that the proposed approach significantly outperforms FedAvg, FedProx, and FedSelect in prediction accuracy and cross-regional fairness, while simultaneously reducing communication costs and energy consumption.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Multiagent Systems: Multiagent Learning
• Humans and AI: Human-in-the-loop Machine Learning

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• Security and Privacy: Large-scale security measurements

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.

Problem

• Enhances federated learning scalability and reduces communication costs
• Improves security through encryption and privacy-preserving techniques
• Optimizes client selection based on utility and energy efficiency

Innovation

• Hierarchical multi-edge FL framework with client selection
• Secure aggregation using homomorphic encryption and differential privacy
• Score-based prioritization of utility, energy efficiency and data sensitivity