单轮联邦学习让设备高效协作，无需反复通信。
Towards One-shot Federated Learning: Advances, Challenges, and Future Directions
- 仅用一轮通信完成模型聚合，降低资源消耗。
- 支持数据异构场景，提升边缘设备训练可行性。
- 适合隐私敏感、算力有限的实时应用，如智能医疗。
单轮联邦学习（One-shot FL）通过单次通信完成协同训练，避免了传统联邦学习中反复通信的开销，特别适用于资源受限和隐私敏感的应用场景。该综述系统梳理了单轮联邦学习的独特运行机制，区别于传统联邦方法。它通过单轮模型聚合，在保障数据本地性的同时，支持资源受限设备的参与。文章对现有方法进行了分类，重点涵盖客户端模型初始化、聚合策略以及处理非独立同分布（non-IID）数据分布的方案。同时分析了当前方法在可扩展性和泛化能力方面的局限，尤其在非IID设置下的表现。通过总结前沿技术并指出开放挑战，本综述旨在为研究人员与实践者提供设计与实现单轮联邦学习系统的全面参考，推动其在真实资源受限环境中的发展与应用。
原文摘要 · Abstract (English)
One-shot FL enables collaborative training in a single round, eliminating the need for iterative communication, making it particularly suitable for use in resource-constrained and privacy-sensitive applications. This survey offers a thorough examination of One-shot FL, highlighting its distinct operational framework compared to traditional federated approaches. One-shot FL supports resource-limited devices by enabling single-round model aggregation while maintaining data locality. The survey systematically categorizes existing methodologies, emphasizing advancements in client model initialization, aggregation techniques, and strategies for managing heterogeneous data distributions. Furthermore, we analyze the limitations of current approaches, particularly in terms of scalability and generalization in non-IID settings. By analyzing cutting-edge techniques and outlining open challenges, this survey aspires to provide a comprehensive reference for researchers and practitioners aiming to design and implement One-shot FL systems, advancing the development and adoption of One-shot FL solutions in a real-world, resource-constrained scenario.
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