Knowing When to Adopt the Shared Knowledge: Benefit-Calibrated Federated LoRA under Partial Participation

Federated low-rank adaptation (FedLoRA) enables communication-efficient fine-tuning without centralizing client data. However, under partial participation, heterogeneous data, and resource constraints, federated collaboration may not benefit all clients simultaneously. Moreover, aggregation may conceal negative transfer, while short-term local signals do not align with long-term benefits reliably. We therefore revisit FedLoRA from a client-benefit perspective and investigate when knowledge should be shared, personalized, or deferred. We define federation benefit relative to local proximal training and propose a benefit-calibrated framework that accumulates evidence across communication rounds and make the decision by Bayesian method. In particular, historical participation is tracked to prevent persistent client under-representation, while each client’s estimated benefit, associated uncertainty, and resource budget jointly determine whether collaboration follows a shared, personalized, or deferred decision. The analysis across multiple datasets shows that the benefits of federation vary substantially across client, motivating client-level evaluation and evidence-based collaboration. Experimental results demonstrate that our method achieves stronger and more consistent performance than existing baselines in most of scenarios.