动态自组织联邦学习框架，自动适应数据变化并优化集群结构。
SOFA-FL: Self-Organizing Hierarchical Federated Learning with Adaptive Clustered Data Sharing
- 通过动态聚类构建初始层次结构，支持系统自我演化。
- 采用嫁接、修剪等操作动态调整网络拓扑以应对数据分布变化。
- 按需共享部分数据，缓解客户端间数据异构问题，适合动态环境。
联邦学习在动态环境中面临数据异构性和固定网络拓扑僵化的问题。本文提出新型框架SOFA-FL（自组织分层联邦学习与自适应集群数据共享），支持分层联邦系统随时间自我组织与适应。该框架包含三个核心机制：(1) 动态多分支聚合聚类（DMAC），用于构建初始高效分层结构；(2) 自组织分层自适应传播与演化（SHAPE），通过嫁接、修剪、整合和净化等原子操作实现拓扑动态重构，以适应数据分布变化；(3) 自适应集群数据共享，允许客户端与簇节点间受控地部分交换数据，缓解数据异构性。结合这些机制，SOFA-FL能有效捕捉客户端间的动态关系，提升个性化能力，且无需预设集群结构。
原文摘要 · Abstract (English)
Federated Learning (FL) faces significant challenges in evolving environments, particularly regarding data heterogeneity and the rigidity of fixed network topologies. To address these issues, this paper proposes \textbf{SOFA-FL} (Self-Organizing Hierarchical Federated Learning with Adaptive Clustered Data Sharing), a novel framework that enables hierarchical federated systems to self-organize and adapt over time. The framework is built upon three core mechanisms: (1) \textbf{Dynamic Multi-branch Agglomerative Clustering (DMAC)}, which constructs an initial efficient hierarchical structure; (2) \textbf{Self-organizing Hierarchical Adaptive Propagation and Evolution (SHAPE)}, which allows the system to dynamically restructure its topology through atomic operations -- grafting, pruning, consolidation, and purification -- to adapt to changes in data distribution; and (3) \textbf{Adaptive Clustered Data Sharing}, which mitigates data heterogeneity by enabling controlled partial data exchange between clients and cluster nodes. By integrating these mechanisms, SOFA-FL effectively captures dynamic relationships among clients and enhances personalization capabilities without relying on predetermined cluster structures.
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