SecHeto-FL: A Secure and Resilient Federated Learning Framework for Heterogeneous IoT Networks

Abstract

This study introduces a semi-asynchronous web browser-based federated learning (FL) framework with secure communication, designed for heterogeneous IoT networks. The study addresses the complex challenges of FL over a large number of heterogeneous IoT devices connected by insecure and unstable communication links compounded with the possibility of client dropout, where clients may disconnect due to internal failures or external reasons, using a model-centric client selection strategy and a semi-asynchronous FL protocol. In the semi-asynchronous process, the central server emulates a synchronous system model by setting a submission deadline for clients and only timely updates are considered for the client selection process. This ensures a robust FL process for the client dropout problem. Unlike agnostic selection protocols, we propose a performance-based client selection algorithm that evaluates local model quality. This maintains fairness and global model performance even with intermittent client participation. Moreover, we utilize TLS-based central server validation and a public key cryptography-based client authentication mechanism to defend against communication layer threats (man-in-the-middle attacks, unauthorized client access) to further strengthen communication security. The proposed framework is hereafter referred to as SecHeto-FL. Extensive evaluation results show that SecHeto-FL significantly outperforms state-of-the-art techniques, in terms of global model accuracy. Theoretical analysis further guarantees convergence of the global model and resilience against client dropout attacks. These findings position SecHeto-FL as a practical and scalable FL framework for next-generation intelligent networks that remains secure in the face of communication security risks.