Jingang Jiang

Heterogeneous Federated Learning with Scalable Server Mixture-of-Experts

Classical Federated Learning (FL) encounters significant challenges when deploying large models on power-constrained clients. To tackle this, we propose an asymmetric FL mechanism that enables the aggregation of compact client models into a comprehensive server model. We design the server model as a