Collaborative Inference for Federated Noisy Labels: Convergence under Dual Heterogeneity

Heterogeneous label noise across clients poses critical challenges to robust training and stable convergence in federated learning. Existing methods largely rely on local label correction, limiting their ability to leverage global information. Moreover, theoretical convergence analysis under dual data-noise heterogeneity remains under-explored. To address this, we propose FedCoIn, a federated noisy label learning framework based on generative collaborative inference. This method treats ground-truth labels as latent variables and performs label inference through global generative modeling, enabling cross-client collaborative noise correction under privacy constraints. Since heterogeneity may disrupt generative inference and undermine global correction consistency, FedCoIn introduces a distribution-aware dynamic prior and a noise-aware weighted aggregation strategy to stabilize label inference and harmonize corrections across clients. Theoretically, we provide convergence analysis under three heterogeneous scenarios, including data homogeneity with noise heterogeneity, data heterogeneity with noise homogeneity, and dual data-noise heterogeneity. This analysis characterizes the impact of both types of heterogeneity on convergence, thereby establishing a new theoretical foundation for federated noisy label learning and providing theoretical support for FedCoIn. Experimental results demonstrate that FedCoIn consistently outperforms state-of-the-art methods in federated noisy label learning tasks.