SOFA-FL: Self-Organizing Hierarchical Federated Learning with Adaptive Clustered Data Sharing

• URL: http://arxiv.org/abs/2512.08267v1
• Date: Tue, 09 Dec 2025 05:47:44 GMT
• Title: SOFA-FL: Self-Organizing Hierarchical Federated Learning with Adaptive Clustered Data Sharing
• Abstract summary: SOFA-FL is a novel framework that enables hierarchical federated systems to self-organize and adapt over time.<n>By integrating these mechanisms, SOFA-FL effectively captures dynamic relationships among clients and enhances personalization capabilities.
• Score: 2.668832984436106
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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