LATENT DOMAIN DISCOVERY FOR DOMAIN-GENERALISED PROTOTYPICAL FEDERATED LEARNING

Federated Learning (FL) enables collaborative model training without sharing raw data, but heterogeneous client distributions can substantially hinder the learning of transferable representations. This challenge is particularly difficult under domain shift, where clients may contain diverse or mixed domain characteristics, while explicit domain labels are often unavailable. SOTA domain-aware FL methods require predefined domain identities, limiting their applicability to such settings. We propose FedLDD, a Latent Domain Discovery framework that jointly discovers latent domain structure from client prototypes and exploits it for domain-aware federated representation learning. Our key insight is that variations in class-wise client prototypes can be factorised into a compact set of latent domain prototypes, while each client can be represented as a soft composition of these latent domains. Specifically, FedLDD factorises the client prototype matrix into a client-to-latent-domain assignment matrix and a latent-domain prototype matrix. The learnt assignments are not treated as domain identities; instead, they provide soft domain supervision for local representation learning. Each client constructs class-specific soft prototypes by combining latent-domain prototypes according to its learnt assignment and uses them to perform domain-consistent prototype alignment and cross-domain prototype contrastive learning. This creates a closed-loop process in which client representations reveal latent domain structure, the discovered structure guides local learning, and the resulting prototypes refine subsequent domain discovery. Extensive experiments on diverse domain-shift benchmarks demonstrate that FedLDD improves federated generalisation while discovering compact latent domain structures. Further analyses show that soft latent-domain supervision effectively captures mixed client characteristics and provides a more flexible representation of cross-client distribution shifts than hard domain assignments.