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.

翻译：物联网与人工智能的融合已在各行业催生创新，但日益增长的隐私关切与数据孤岛问题阻碍了进展。传统集中式机器学习难以应对这些挑战，这推动了联邦学习（FL）的兴起——一种去中心化范式，可在不共享本地原始数据的情况下实现协作式模型训练。FL确保了数据隐私、降低了通信开销并支持可扩展性，然而相较于集中式方法，其异构性带来了额外的复杂性。本综述聚焦于FL的三个主要研究方向：个性化、优化与鲁棒性，通过结合文献计量分析与系统综述的混合方法，提供结构化分类以识别最具影响力的工作。我们探讨了与异构性、效率、安全性及隐私相关的挑战与技术，并对聚合策略（包括架构、同步方法及多样化的联邦目标）进行了全面概述。作为补充，我们讨论了实际评估方法，并展示了在独立同分布与非独立同分布数据分布下比较聚合方法的实验。最后，我们概述了推动FL发展的前瞻性研究方向，旨在指导这一快速演进领域的未来创新。