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A novel deep reinforcement learning method that jointly optimizes client selection and local training in heterogeneous environments.
Experimental results demonstrate that our proposed method achieves significant energy cost savings, up to 77%, compared to baseline methods. Moreover, our ...
Nov 22, 2024 · A joint optimization problem is built for client selection and resource allocation to improve the learning efficiency by considering imperfect ...
Joint Client Selection and Training optimization for Energy-Efficient Federated Learning. Kang Yan, Nina Shu, Tao Wu, Chunsheng Liu, Jun Huang, Jingbo Yu.
Apr 18, 2023 · In federated learning (FL), distributed clients can collaboratively train a shared global model while retaining their own training data locally.
Missing: Energy- | Show results with:Energy-
Dec 6, 2023 · This joint optimization aims to determine FL participants and the learning schedule for each FL model such that the total training cost of all ...
Nov 22, 2024 · A joint optimization problem is built for client selection and resource allocation to improve the learning efficiency by considering imperfect ...
This paper studies the problem of client selection and resource allocation to minimize the energy consumption and learning time of multiple FL jobs competing ...
Sep 7, 2024 · ... learning algorithm can shorten the model training time and improve efficiency compared to the traditional federated learning algorithm.
Aug 10, 2021 · This article proposes to dynamically adjust and optimize the tradeoff between maximizing the number of selected clients and minimizing the total energy ...