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

TL;DR

SOFA-FL introduces a self-organizing hierarchical federated learning framework that dynamically adapts network topology and data sharing to handle data heterogeneity and evolving environments.

Contribution

It proposes novel mechanisms for self-organizing hierarchical structures and adaptive data sharing, addressing data heterogeneity and network rigidity in federated learning.

Findings

Effective hierarchical structure construction via DMAC

Dynamic topology adaptation through SHAPE

Improved personalization and data sharing efficiency

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.