Joint Client Scheduling and Resource Allocation under Channel Uncertainty in Federated Learning

• URL: http://arxiv.org/abs/2106.06796v1
• Date: Sat, 12 Jun 2021 15:18:48 GMT
• Title: Joint Client Scheduling and Resource Allocation under Channel Uncertainty in Federated Learning
• Authors: Madhusanka Manimel Wadu, Sumudu Samarakoon, Mehdi Bennis
• Abstract summary: Federated learning (FL) over wireless networks depends on the reliability of the client-server connectivity and clients' local computation capabilities. In this article, we investigate the problem of client scheduling and resource block (RB) allocation to enhance the performance of model training using FL. A proposed method reduces the gap of the training accuracy loss by up to 40.7% compared to state-of-theart client scheduling and RB allocation methods.
• Score: 47.97586668316476
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The performance of federated learning (FL) over wireless networks depend on the reliability of the client-server connectivity and clients' local computation capabilities. In this article we investigate the problem of client scheduling and resource block (RB) allocation to enhance the performance of model training using FL, over a pre-defined training duration under imperfect channel state information (CSI) and limited local computing resources. First, we analytically derive the gap between the training losses of FL with clients scheduling and a centralized training method for a given training duration. Then, we formulate the gap of the training loss minimization over client scheduling and RB allocation as a stochastic optimization problem and solve it using Lyapunov optimization. A Gaussian process regression-based channel prediction method is leveraged to learn and track the wireless channel, in which, the clients' CSI predictions and computing power are incorporated into the scheduling decision. Using an extensive set of simulations, we validate the robustness of the proposed method under both perfect and imperfect CSI over an array of diverse data distributions. Results show that the proposed method reduces the gap of the training accuracy loss by up to 40.7% compared to state-of-theart client scheduling and RB allocation methods.

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