Abstract
This paper studies the energy and time resource optimization of federated learning (FL) in wireless communication networks. In the considered network model, each client uses local data for model training, and then sends the trained FL model to the central server. However, the energy budget of the local computing and transmission process is limited. Therefore, reducing energy consumption should be given priority when we consider the FL efficiency and accuracy. We invest a green communication joint learning issue and expressed as an optimization problem. To minimize the energy consumption under the condition that the overall FL time is constrained, we propose an iterative algorithm based on Lyapunov optimization. Our algorithm selects the clients participating in each round and allocates the different bandwidth to each client. At the same time, the connection between local training and communication process is considered so that we can get the optimal client local calculation force.
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Chu, X. et al. (2022). Joint Accuracy and Resource Allocation for Green Federated Learning Networks. In: Qiu, M., Gai, K., Qiu, H. (eds) Smart Computing and Communication. SmartCom 2021. Lecture Notes in Computer Science, vol 13202. Springer, Cham. https://doi.org/10.1007/978-3-030-97774-0_14
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DOI: https://doi.org/10.1007/978-3-030-97774-0_14
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