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

arXiv: https://arxiv.org/abs/2312.14941 · HTML: https://mlsanity.com/paper/2312.14941/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 30 references that are in the map.
- First posted on arXiv 5 Dec 2023.

## Builds on (most cited first, 13 of 13)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge](https://mlsanity.com/paper/1804.08333/) (2018)
- [Deep Reinforcement Learning framework for Autonomous Driving](https://mlsanity.com/paper/1704.02532/) (2017)
- [Optimal Client Sampling for Federated Learning](https://mlsanity.com/paper/2010.13723/) (2020)
- [Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learning](https://mlsanity.com/paper/2105.05883/) (2021)
- [An Efficiency-boosting Client Selection Scheme for Federated Learning with Fairness Guarantee](https://mlsanity.com/paper/2011.01783/) (2020)
- [MLlib: Machine Learning in Apache Spark](https://mlsanity.com/paper/1505.06807/) (2015)
- [E.T.-RNN: Applying Deep Learning to Credit Loan Applications](https://mlsanity.com/paper/1911.02496/) (2019)
- [Data Heterogeneity-Robust Federated Learning via Group Client Selection in Industrial IoT](https://mlsanity.com/paper/2202.01512/) (2022)
- [FLaaS: Federated Learning as a Service](https://mlsanity.com/paper/2011.09359/) (2020)
- [Context-Aware Online Client Selection for Hierarchical Federated Learning](https://mlsanity.com/paper/2112.00925/) (2021)

## Built on by (most cited first, 2 of 2)

- [Deconstructing The Ethics of Large Language Models from Long-standing Issues to New-emerging Dilemmas: A Survey](https://mlsanity.com/paper/2406.05392/) (2024)
- [Adaptive Composition of Machine Learning as a Service (MLaaS) for IoT Environments](https://mlsanity.com/paper/2506.11054/) (2025)

## Abstract (arXiv)

Federated Learning (FL) enables multiple clients to train machine learning models collaboratively without sharing the raw training data. However, for a given FL task, how to select a group of appropriate clients fairly becomes a challenging problem due to budget restrictions and client heterogeneity. In this paper, we propose a multi-criteria client selection and scheduling scheme with a fairness guarantee, comprising two stages: 1) preliminary client pool selection, and 2) per-round client scheduling. Specifically, we first define a client selection metric informed by several criteria, such as client resources, data quality, and client behaviors. Then, we formulate the initial client pool selection problem into an optimization problem that aims to maximize the overall scores of selected clients within a given budget and propose a greedy algorithm to solve it. To guarantee fairness, we further formulate the per-round client scheduling problem and propose a heuristic algorithm to divide the client pool into several subsets such that every client is selected at least once while guaranteeing that the `integrated' dataset in a subset is close to an independent and identical distribution (iid). Our experimental results show that our scheme can improve the model quality especially when data are non-iid.