系统梳理联邦学习聚合方法与未来方向，助你快速掌握核心进展。
Federated Learning Survey: A Multi-Level Taxonomy of Aggregation Techniques, Experimental Insights, and Future Frontiers
- 构建多层级分类体系，整合文献计量与系统综述
- 对比分析异构数据下不同聚合策略的性能表现
- 面向隐私、效率与鲁棒性，指明前沿研究路径
物联网与人工智能的融合推动了各行业创新，但日益增长的隐私担忧和数据隔离问题制约发展。传统集中式机器学习难以应对这些挑战，催生了联邦学习（FL）这一去中心化范式，可在不共享本地原始数据的前提下实现协同模型训练。FL保障数据隐私、降低通信开销并支持可扩展性，但相比集中式方法面临更高的异构性复杂度。本综述聚焦个性化、优化与鲁棒性三大研究方向，采用文献计量分析与系统综述相结合的混合方法，识别出最具影响力的成果。我们探讨异构性、效率、安全与隐私相关挑战及应对技术，全面梳理聚合策略，涵盖架构设计、同步机制与多样化联邦目标。此外，讨论实际评估方法，并在IID与non-IID数据分布下比较多种聚合方法的实验结果。最后，提出有前景的研究方向，旨在引导该快速演进领域的未来创新。
原文摘要 · Abstract (English)
The integration of IoT and AI has unlocked innovation across industries, but growing privacy concerns and data isolation hinder progress. Traditional centralized ML struggles to overcome these challenges, which has led to the rise of Federated Learning (FL), a decentralized paradigm that enables collaborative model training without sharing local raw data. FL ensures data privacy, reduces communication overhead, and supports scalability, yet its heterogeneity adds complexity compared to centralized approaches. This survey focuses on three main FL research directions: personalization, optimization, and robustness, offering a structured classification through a hybrid methodology that combines bibliometric analysis with systematic review to identify the most influential works. We examine challenges and techniques related to heterogeneity, efficiency, security, and privacy, and provide a comprehensive overview of aggregation strategies, including architectures, synchronization methods, and diverse federation objectives. To complement this, we discuss practical evaluation approaches and present experiments comparing aggregation methods under IID and non-IID data distributions. Finally, we outline promising research directions to advance FL, aiming to guide future innovation in this rapidly evolving field.
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