FL-EGM: Decentralized Federated Learning using Aggregator Selection with Enhanced Global Model

Federated Learning (FL) has emerged as a promising solution to address data privacy concerns, but it also faces ob-stacles, including biased centralized aggregation. In this paper, we propose a novel decentralized FL architecture with round-based aggregator selection that enhances the accuracy of the global model while preserving data privacy for the users. Our proposed model consists of three distinct phases. Firstly, we address the issue of a central aggregator by introducing a model where we periodically select the aggregator as the best-performing client from participant nodes. Secondly, we train all clients using their respective data but excluding the client selected as an aggregator. In the third phase, we further refine the global model by training it on the raw data of the selected aggregator, leading to an enhanced global model. These three modules empower the proposed model to offer improved accuracy and decentralization. Our implementation yielded promising results, demonstrating that the proposed mode achieves an accuracy of 98.54 % along with enhanced decentralization. The proposed model has also validated different datasets and network conditions, such as the number of participant nodes. Furthermore, the performance of the proposed model is validated in the presence of a biased aggregator. The results demonstrate that the proposed model achieves 98.43 % accuracy and shows more robustness in the presence of a biased aggregator. Finally, the proposed model converges fast compared to other baseline models.