Rethinking Heterogeneous Federated Learning: Low-Rank Continuous Feature Representations for Knowledge Sharing

Real-world federated learning deployments are often model-heterogeneous, rendering parameter averaging ineffective. Existing methods mainly adopt two mechanisms: one introduces homogeneous proxy models for knowledge aggregation and distillation, but incurs high computation and communication costs; the other shares class prototypes, which are lightweight but compress intra-class structure and are prone to feature collapse and semantic mismatch. Therefore, existing methods exhibit a clear expressiveness–efficiency gap. We propose FedCF, which leverages lightweight yet expressive low-rank continuous feature fields to support heterogeneous collaboration, preventing feature collapse while maintaining low overhead. The server aligns and fuses heterogeneous local fields in a canonical function space to form a queryable global field, providing clients with rich feature supervision to mitigate collapse. Local training further adopts feature–logit dual distillation to jointly align representations and calibrate decision boundaries, without proxy models or public data, keeping the overhead low. Across heterogeneous benchmarks, FedCF achieves a better accuracy–efficiency trade-off.