Joint Client Selection and Bandwidth Allocation Algorithm for Federated Learning

doi:10.1109/TMC.2021.3136611

Joint Client Selection and Bandwidth Allocation Algorithm for Federated Learning

2023-06-01

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AI

DOI：10.1109/TMC.2021.3136611

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In federated learning (FL), if the participating mobile devices have low computing power and poor wireless channel conditions and/or they do not have sufficient data for various classes, a long convergence time is required to achieve the desired model accuracy. To address this problem, we first formulate a constrained Markov decision process (CMDP) problem that aims to minimize the average time of rounds while maintaining the numbers of trained data and trained data classes above certain numbers. To obtain the optimal scheduling policy, the formulated CMDP problem is converted into an equivalent linear programming (LP). Additionally, to overcome the problem of the curse of dimensionality in CMDP, we develop a joint client selection and bandwidth allocation algorithm (JCSBA) that jointly selects appropriate mobile devices and allocates suitable amount of bandwidth to them at each round by considering their data information, computing power, and channel gain. Evaluation results validate that J-CSBA can reduce the convergence time by up to 49% compared to a conventional random scheme.

Keyword:

Mobile handsets

Servers

Data models

Convergence

Computational modeling

Channel allocation

Bandwidth

Federated learning (FL)

joint optimization

client selection

bandwidth allocation

convergence time

constrained Markov decision process (CMDP)

Joint Client Selection and Bandwidth Allocation Algorithm for Federated Learning

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