Xinyi Lu

FedSMU: Communication-Efficient and Generalization-Enhanced Federated Learning through Symbolic Model Updates

The significant communication overhead and client data heterogeneity have posed an important challenge to current federated learning (FL) paradigm. Existing compression-based and optimization-based FL algorithms typically focus on addressing either the model compression challenge or the data heterog…