FedLPA: Local Prior Alignment for Heterogeneous Federated Generalized Category Discovery
Geeho Kim, Jinu Lee, Bohyung Han
We propose a novel federated learning framework to effectively address the challenges of generalized category discovery (GCD) in heterogeneous federated learning environments.Our algorithm does not require unrealistic assumptions often made in existing GCD work, such as the need for prior knowledge of novel class counts or the assumption of uniform class distributions.Within our federated pipeline, each client estimates its unique category distribution and the number of local classes by constructing a local, graph-based data representation. Leveraging the category structures identified via clustering, we propose a novel self-distillation strategy, which regularizes the model to align its batch-wise predictions for unlabeled examples directly with these discovered local category structures.This synergy of local structure grounding and dynamic prior adaptation enables robust generalized category discovery under severe data heterogeneity. Our framework significantly outperforms existing federated generalized category discovery approaches on fine-grained and standard benchmarks, as demonstrated by extensive experimental results.