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Fedgcs: A generative framework for efficient client selection in federated learning via gradient-based optimization
Federated Learning faces significant challenges in statistical and system heterogeneity,
along with high energy consumption, necessitating efficient client selection strategies …
along with high energy consumption, necessitating efficient client selection strategies …
Ranking-based client imitation selection for efficient federated learning
Federated Learning (FL) enables multiple devices to collaboratively train a shared model
while ensuring data privacy. The selection of participating devices in each training round …
while ensuring data privacy. The selection of participating devices in each training round …
Breaking the Memory Wall for Heterogeneous Federated Learning via Model Splitting
Federated Learning (FL) enables multiple devices to collaboratively train a shared model
while preserving data privacy. Ever-increasing model complexity coupled with limited …
while preserving data privacy. Ever-increasing model complexity coupled with limited …
Ranking-based Client Selection with Imitation Learning for Efficient Federated Learning
Federated Learning (FL) enables multiple devices to collaboratively train a shared model
while ensuring data privacy. The selection of participating devices in each training round …
while ensuring data privacy. The selection of participating devices in each training round …
Reinforcement Learning-based Dual-Identity Double Auction in Personalized Federated Learning
Federated learning participants have two identities: model trainers and model users. As
model users, participants care most about the performance of the final model on their own …
model users, participants care most about the performance of the final model on their own …