Sequential Federated Learning in Hierarchical Architecture on Non-IID
Datasets
Sequential Federated Learning in Hierarchical Architecture on Non-IID
Datasets
In a real federated learning (FL) system, communication overhead for passing model parameters between the clients and the parameter server (PS) is often a bottleneck. Hierarchical federated learning (HFL) that poses multiple edge servers (ESs) between clients and the PS can partially alleviate communication pressure but still needs the aggregation …