FedMint: Intelligent Bilateral Client Selection in Federated Learning with Newcomer IoT Devices

Document Type

Article

Source of Publication

IEEE Internet of Things Journal

Publication Date

6-7-2023

Abstract

Federated Learning (FL) is a novel distributed privacy-preserving learning paradigm, which enables the collaboration among several participants (e.g., Internet of Things devices) for the training of machine learning models. However, selecting the participants that would contribute to this collaborative training is highly challenging. Adopting a random selection strategy would entail substantial problems due to the heterogeneity in terms of data quality, and computational and communication resources across the participants. Although several approaches have been proposed in the literature to overcome the problem of random selection, most of these approaches follow a unilateral selection strategy. In fact, they base their selection strategy on only the federated server’s side, while overlooking the interests of the client devices in the process. To overcome this problem, we present in this paper FedMint, an intelligent client selection approach for federated learning on IoT devices using game theory and bootstrapping mechanism. Our solution involves the design of: (1) preference functions for the client IoT devices and federated servers to allow them to rank each other according to several factors such as accuracy and price, (2) intelligent matching algorithms that take into account the preferences of both parties in their design, and (3) bootstrapping technique that capitalizes on the collaboration of multiple federated servers in order to assign initial accuracy value for the newly connected IoT devices. We compare our approach against the VanillaFL selection process as well as other state-of-the-art approach and showcase the superiority of our proposal.

ISSN

2372-4662

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Volume

PP

Issue

99

First Page

1

Last Page

1

Disciplines

Computer Sciences

Keywords

Servers, Internet of Things, Federated learning, Training, Task analysis, Games, Game theory

Indexed in Scopus

no

Open Access

yes

Open Access Type

Green: A manuscript of this publication is openly available in a repository

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