GQFedWAvg: Optimization-Based Quantized Federated Learning in General Edge Computing Systems

• URL: http://arxiv.org/abs/2306.07497v2
• Date: Sat, 18 Nov 2023 11:43:33 GMT
• Title: GQFedWAvg: Optimization-Based Quantized Federated Learning in General Edge Computing Systems
• Authors: Yangchen Li, Ying Cui, and Vincent Lau
• Abstract summary: The optimal implementation of federated learning (FL) in practical edge computing has been an outstanding problem. We propose an optimization quantized FL algorithm, which can appropriately fit a general edge computing system with uniform or nonuniform computing and communication systems.
• Score: 11.177402054314674
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The optimal implementation of federated learning (FL) in practical edge computing systems has been an outstanding problem. In this paper, we propose an optimization-based quantized FL algorithm, which can appropriately fit a general edge computing system with uniform or nonuniform computing and communication resources at the workers. Specifically, we first present a new random quantization scheme and analyze its properties. Then, we propose a general quantized FL algorithm, namely GQFedWAvg. Specifically, GQFedWAvg applies the proposed quantization scheme to quantize wisely chosen model update-related vectors and adopts a generalized mini-batch stochastic gradient descent (SGD) method with the weighted average local model updates in global model aggregation. Besides, GQFedWAvg has several adjustable algorithm parameters to flexibly adapt to the computing and communication resources at the server and workers. We also analyze the convergence of GQFedWAvg. Next, we optimize the algorithm parameters of GQFedWAvg to minimize the convergence error under the time and energy constraints. We successfully tackle the challenging non-convex problem using general inner approximation (GIA) and multiple delicate tricks. Finally, we interpret GQFedWAvg's function principle and show its considerable gains over existing FL algorithms using numerical results.

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