Enhancing Federated Learning Performance through Adaptive Client Optimization with Hyperparameter Tuning

Abstract

The effectiveness of Industrial Internet of Things (IIoT) systems requires a robust fault detection mechanism, a task effectively accomplished by leveraging Artificial Intelligence (AI). However, the current centralized learning approach proves inadequate. In response to this limitation, Federated Learning (FL) enables decentralized training, ensuring the protection of individual data. The traditional FL settings are not sufficient to provide an effective learning process, which needs to be refined. This paper introduces an Adaptive Distributed Client Training (ADCT) mechanism designed to optimize performance for each FL participant, thereby establishing an efficient and resilient system. The proposed ADCT utilizes two parameters, namely the accuracy threshold and grid search step, to find the optimal hyperparameter for each client in a specific number of federation rounds. The evaluation results, conducted using the MNIST and FMNIST datasets in non-IID settings, indicate that the proposed ADCT enhances the F1-score by up to 37.13% compared to state-of-the-art methods.