透過多分支網路增強聯邦學習的通訊效率和訓練時間均勻性

Abstract

在本研究中，我們提出了一種聯邦學習的方法，將多分支網路與Oort客戶端選擇算法結合，以提高聯邦學習系統的性能。該方法成功地解決了非獨立同分布（non-iid）數據的重要問題，這是MFedAvg方法尚未充分解決的挑戰。此外，本研究的一項關鍵創新是引入了一種名為"均勻性"的度量，用以量化聯邦學習環境中參與者之間訓練時間的差異。這一新概念不僅有助於識別落後者，而且為評估系統的公平性和效率提供了值得參考的見解。實驗結果強調了將多分支網路與oort客戶端選擇算法集成的優點，並凸顯了在設計和評估聯邦學習系統中，"均勻性"的關鍵作用。

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 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 the assessing 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.