PDCFL: Prototype‐Driven Decoupled Clustered Federated Learning for Non‐IID Data
TL;DR
A prototype space optimization mechanism based on NT‐Xent that alleviates insufficient prototype discriminability by promoting intra‐cluster aggregation and increasing intercluster distance is devised and an adaptive model decoupling strategy is introduced to address the remaining intra‐cluster heterogeneity.
Abstract
Federated Learning (FL) is prone to convergence instability and degraded personalization capability under non‐independent and identically distributed (non‐IID) data conditions. This article proposes a prototype‐driven decoupled clustered federated learning (PDCFL) framework to address the limitations of existing prototype‐based methods in terms of clustering robustness, prototype discriminability, and cluster‐level personalization. Hybrid clustering based on the prototype method is proposed, which combines DBSCAN to filter out low‐density client prototypes and leverages spectral clustering to capture nonlinear relationships in the prototype space, thereby achieving robust client clustering. However, to mitigate the suboptimal global knowledge caused by prototype discrimination degradation, we devise a prototype space optimization mechanism based on NT‐Xent that alleviates insufficient prototype discriminability by promoting intra‐cluster aggregation and increasing intercluster distance. Furthermore, an adaptive model decoupling strategy is introduced to address the remaining intra‐cluster heterogeneity. This strategy dynamically adjusts the ratio of shared parameters to personalized parameters according to the similarity between client prototypes and cluster center prototypes, thereby constructing a fine‐grained personalized model for each client. On the Digits‐5 dataset under the feature & label shift setting, PDCFL achieves an accuracy improvement of 3.52%; under the label shift setting, the method achieves an average improvement of 9.05% across five domains.