Whom to Trust? Adaptive Collaboration in Personalized Federated Learning

Data heterogeneity poses a fundamental challenge in federated learning (FL), particularly when clients differ in both their data distributions and the reliability of their predictions. We observe that many personalized FL (PFL) methods fail to outperform the essential local and centralized baselines, revealing a meaningful personalization regime in which a single global model is insufficient, yet cross-client collaboration remains useful. From this diagnosis, we identify two requirements for effective personalization: adaptivity in how strongly each client relies on collaboration and fine-grained selectivity in whose predictions should be trusted. We instantiate these principles in FEDMOSAIC, a personalized federated co-training method that dynamically balances local and collaborative objectives while weighting client contributions at the example level. Across label-skew, feature-shift, and hybrid settings, FEDMOSAIC outperforms strong FL and PFL baselines and frequently surpasses both local and centralized training. We further establish convergence under standard smoothness, bounded-variance, and pseudo-label drift assumptions, and characterize a client-level differentially private variant of the protocol. These results clarify when federated personalization is useful and how adaptive, selective collaboration can realize its benefits.