Juan, P.-H.; Wu, J.-L. Enhancing Communication Efficiency and Training Time Uniformity in Federated Learning through Multi-Branch Networks and the Oort Algorithm. Algorithms2024, 17, 52.
Juan, P.-H.; Wu, J.-L. Enhancing Communication Efficiency and Training Time Uniformity in Federated Learning through Multi-Branch Networks and the Oort Algorithm. Algorithms 2024, 17, 52.
Juan, P.-H.; Wu, J.-L. Enhancing Communication Efficiency and Training Time Uniformity in Federated Learning through Multi-Branch Networks and the Oort Algorithm. Algorithms2024, 17, 52.
Juan, P.-H.; Wu, J.-L. Enhancing Communication Efficiency and Training Time Uniformity in Federated Learning through Multi-Branch Networks and the Oort Algorithm. Algorithms 2024, 17, 52.
Abstract
In this study, we present a federated learning approach that combines a multi-branch network and the Oort client selection algorithm to improve the performance of federated learning systems. This method successfully addresses the significant issue of non-iid data, a challenge not adequately tackled by the commonly used MFedAvg method. Additionally, one of the key innovations of this research is the introduction of uniformity, a metric that quantifies the disparity in training time amongst participants in a federated learning setup. This novel concept not only aids in identifying stragglers but also provides valuable insights into assessing the fairness and efficiency of the system. Experimental results underscore the merits of the integrated multi-branch network with the Oort client selection algorithm and highlight the crucial role of uniformity in designing and evaluating federated learning systems.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
