One-shot Federated Learning Methods: A Practical Guide

• URL: http://arxiv.org/abs/2502.09104v1
• Date: Thu, 13 Feb 2025 09:26:44 GMT
• Title: One-shot Federated Learning Methods: A Practical Guide
• Authors: Xiang Liu, Zhenheng Tang, Xia Li, Yijun Song, Sijie Ji, Zemin Liu, Bo Han, Linshan Jiang, Jialin Li,
• Abstract summary: One-shot Federated Learning (OFL) is a distributed machine learning paradigm that constrains client-server communication to a single round. This paper presents a systematic analysis of the challenges faced by OFL and thoroughly reviews the current methods.
• Score: 23.737787001337082
• License:
• Abstract: One-shot Federated Learning (OFL) is a distributed machine learning paradigm that constrains client-server communication to a single round, addressing privacy and communication overhead issues associated with multiple rounds of data exchange in traditional Federated Learning (FL). OFL demonstrates the practical potential for integration with future approaches that require collaborative training models, such as large language models (LLMs). However, current OFL methods face two major challenges: data heterogeneity and model heterogeneity, which result in subpar performance compared to conventional FL methods. Worse still, despite numerous studies addressing these limitations, a comprehensive summary is still lacking. To address these gaps, this paper presents a systematic analysis of the challenges faced by OFL and thoroughly reviews the current methods. We also offer an innovative categorization method and analyze the trade-offs of various techniques. Additionally, we discuss the most promising future directions and the technologies that should be integrated into the OFL field. This work aims to provide guidance and insights for future research.

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