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Federated Learning (FL) enables multiple clients to train machine learningmodels collaboratively without sharing the raw training data. However, for agiven FL task, how to select a group of appropriate clients fairly becomes achallenging problem due to budget restrictions and client heterogeneity. Inthis paper, we propose a multi-criteria client selection and scheduling schemewith a fairness guarantee, comprising two stages: 1) preliminary client poolselection, and 2) per-round client scheduling. Specifically, we first define aclient selection metric informed by several criteria, such as client resources,data quality, and client behaviors. Then, we formulate the initial client poolselection problem into an optimization problem that aims to maximize theoverall scores of selected clients within a given budget and propose a greedyalgorithm to solve it. To guarantee fairness, we further formulate theper-round client scheduling problem and propose a heuristic algorithm to dividethe client pool into several subsets such that every client is selected atleast once while guaranteeing that the `integrated' dataset in a subset isclose to an independent and identical distribution (iid). Our experimentalresults show that our scheme can improve the model quality especially when dataare non-iid.

Multi-Criteria Client Selection and Scheduling with Fairness Guarantee for Federated Learning Service