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Found 2 papers, 0 papers with code
MAB-based Client Selection for Federated Learning with Uncertain Resources in Mobile Networks
This paper proposes a multi-armed bandit (MAB)-based client selection method to solve the exploration and exploitation trade-off and reduce the time consumption for FL in mobile networks.
Networking and Internet Architecture
Hybrid-FL for Wireless Networks: Cooperative Learning Mechanism Using Non-IID Data
Therefore, to mitigate the degradation induced by non-IID data, we assume that a limited number (e. g., less than 1%) of clients allow their data to be uploaded to a server, and we propose a hybrid learning mechanism referred to as Hybrid-FL, wherein the server updates the model using the data gathered from the clients and aggregates the model with the models trained by clients.
