Utility-Driven Clustered Federated Learning via Class-wise Interaction Attribution

Clustered Federated Learning (CFL) mitigates statistical heterogeneity by grouping clients with similar data distributions. However, existing CFL frameworks predominantly rely on proxy metrics, such as gradient or data similarities, which collapse fine-grained class-level similarity into macroscopic client-level scores. This coarse-grained aggregation inherently masks critical class-wise conflicts, inadvertently exposing the federation to severe negative transfer. In this paper, we propose a fundamental paradigm shift from heuristic similarity to explicit value attribution. We introduce FedCap, a novel utility-driven CFL framework that explicitly quantifies class-wise marginal utilities to capture the true generalization impact of local updates. Guided by this attribution mechanism, FedCap employs a conflict-aware clustering algorithm to systematically eliminate intra-cluster negative transfer, while orchestrating safe, supply-demand-guided representation sharing across clusters. Crucially, we provide rigorous theoretical guarantees demonstrating the robustness of our underlying attribution mechanism against four major types of distribution shifts. Extensive experiments across diverse heterogeneity settings show that FedCap consistently achieves state-of-the-art accuracy. The code is available for anonymous access at https://anonymous.4open.science/r/FedCap-605E.