2026
Enhancing Communication Compression via Discrepancy-aware Calibration for Federated Learning
ICLR 2026poster
Federated Learning (FL) offers a privacy-preserving paradigm for distributed model training by enabling clients to collaboratively learn a shared model without exchanging their raw data. However, the communication overhead associated with exchanging model updates remains a critical challenge, partic…