Federated Learning (FL) enables collaborative learning without directly sharing individual's raw data. FL can be implemented in either a centralized (server-based) or decentralized (peer-to-peer) manner. In this survey, we present a novel perspective: the fundamental difference between centralized FL (CFL) and decentralized FL (DFL) is not merely the network topology, but the underlying training protocol: separate aggregation vs. joint optimization. We argue that this distinction in protocol leads to significant differences in model utility, privacy preservation, and robustness to attacks. We systematically review and categorize existing works in both CFL and DFL according to the type of protocol they employ. This taxonomy provides deeper insights into prior research and clarifies how various approaches relate or differ. Through our analysis, we identify key gaps in the literature. In particular, we observe a surprising lack of exploration of DFL approaches based on distributed optimization methods, despite their potential advantages. We highlight this under-explored direction and call for more research on leveraging distributed optimization for federated learning. Overall, this work offers a comprehensive overview from centralized to decentralized FL, sheds new light on the core distinctions between approaches, and outlines open challenges and future directions for the field.

该研究探讨了联邦学习中集中式与去中心化的根本区别，不仅在于网络拓扑，而在于基础训练协议的不同。本研究首次系统性地评估并分类了现有的集中式和去中心化联邦学习工作，并指出了文献中的关键空白，特别是在基于分布式优化方法的去中心化联邦学习的潜力未被充分探索。这项工作为联邦学习领域提供了新的视角和未来研究方向。

从集中式到去中心化的联邦学习：理论洞察、隐私保护和鲁棒性挑战