单轮通信的联邦学习，解决隐私与通信开销问题。
One-shot Federated Learning Methods: A Practical Guide
- 仅用一轮通信完成模型协作训练，降低隐私风险。
- 现有方法在数据和模型异构下性能显著下降。
- 系统梳理方法分类与未来方向，适合新研究者参考。
单轮联邦学习（OFL）是一种分布式机器学习范式，将客户端与服务器间的通信限制为单轮，解决了传统联邦学习中多轮数据交换带来的隐私和通信开销问题。OFL在与大语言模型等需协同训练的未来技术结合方面展现出实际潜力。然而，当前OFL方法面临两大挑战：数据异构性和模型异构性，导致其性能远低于传统联邦学习。尽管已有大量研究尝试克服这些局限，但尚缺乏系统性总结。本文对OFL面临的挑战进行了系统分析，全面回顾了现有方法，提出一种创新的分类方式，并深入探讨了各类技术的权衡。此外，还讨论了最具前景的未来方向及应融入OFL领域的关键技术。本工作旨在为后续研究提供指导与洞见。
原文摘要 · Abstract (English)
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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