FedHSP: A Robust Federated Learning Framework Coherently Addressing Heterogeneity, Security, and Performance Challenges
Divya G. Nair, C V Aswartha Narayana, K. Jaideep Reddy, Jyothisha J. Nair · IEEE Access · 2025
Federated Learning (FL) is a machine learning training method that leverages local model gradients instead of accessing private data from individual clients, ensuring privacy. However, the practical implementation of FL faces significant challenges. Heterogeneous clients and edge devices with varying computational abilities and unreliable communication channels introduce latency issues to the algorithm. Furthermore, the algorithm is susceptible to attacks from malicious clients, allowing them to insert unwanted updates while benefiting from the global model. These challenges severely impact the algorithm’s performance, rendering it unsuitable for real-time applications. To address these issues, we propose FedHSP, a comprehensive system that tackles device heterogeneity and protects against various forms of attacks. FedHSP incorporates multiple model complexities to accommodate heterogeneous clients. Additionally, it employs a Variational Auto Encoder with dynamic thresholding to detect and eliminate malicious clients. In this paper, we demonstrate FedHSP’s effectiveness in detecting model poisoning attacks. We also show the mitigation of malicious model updates sent to the server. The evaluation is done using the MNIST dataset under various settings. Our experiment results show the drastic performance deviation due to attacks and the successful detection and mitigation with the proposed system.