JinSoo Kim, SeMo Yang, KangYoon Lee, KwangKee Lee, Journal of Internet Computing and Services, Vol. 24, No. 4, pp. 1-13, Aug. 2023
10.7472/jksii.2023.24.4.1, Full Text:  HTML
Keywords: Federated learning, Distributed Learning, FL Framework, Open Source

Abstract

As federated learning brings a large paradigm to modern artificial intelligence research, efforts are being made to incorporate federated learning into research in various fields. However, researchers who apply federated learning face the problem of choosing a federated learning framework and benchmark tool suitable for their situation and purpose. This study aims to present guidelines for selection of federated learning frameworks and benchmark tools considering the circumstances of researchers who apply federated learning in practice. In particular, there are three main contributions in this study. First, it generalizes the situation of the researcher applying federated learning by combining it with the goal of federated learning and proposes guidelines for selecting a federated learning framework suitable for each situation. Second, it shows the suitability of selection by comparing the characteristics and performance of each federated learning framework to the researcher. Finally, the limitations of the existing federated learning framework and a plan for real-world federated learning operation are proposed.

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