SOFA-FL: Self-Organizing Hierarchical Federated Learning with Adaptive Clustered Data Sharing
This work addresses evolving data environments in federated learning, offering a novel approach to enhance adaptability and personalization, though it appears incremental in building on existing hierarchical FL methods.
The paper tackled the challenges of data heterogeneity and rigid network topologies in federated learning by proposing SOFA-FL, a framework that enables self-organizing hierarchical systems with adaptive clustered data sharing, resulting in improved personalization and dynamic adaptation without predetermined structures.
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.