# Proportional Fairness in Federated Learning

arXiv: https://arxiv.org/abs/2202.01666 · HTML: https://mlsanity.com/paper/2202.01666/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 79 references that are in the map.
- First posted on arXiv 3 Feb 2022.

## Builds on (most cited first, 30 of 33)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [Group Normalization](https://mlsanity.com/paper/1803.08494/) (2018)
- [On the Convergence of FedAvg on Non-IID Data](https://mlsanity.com/paper/1907.02189/) (2019)
- [Adaptive Federated Optimization](https://mlsanity.com/paper/2003.00295/) (2020)
- [Counterfactual Fairness](https://mlsanity.com/paper/1703.06856/) (2017)
- [LEAF: A Benchmark for Federated Settings](https://mlsanity.com/paper/1812.01097/) (2018)
- [Stochastic First- and Zeroth-order Methods for Nonconvex Stochastic Programming](https://mlsanity.com/paper/1309.5549/) (2013)
- [Ditto: Fair and Robust Federated Learning Through Personalization](https://mlsanity.com/paper/2012.04221/) (2020)
- [Federated Learning with Matched Averaging](https://mlsanity.com/paper/2002.06440/) (2020)
- [Agnostic Federated Learning](https://mlsanity.com/paper/1902.00146/) (2019)
- [Flower: A Friendly Federated Learning Research Framework](https://mlsanity.com/paper/2007.14390/) (2020)
- [On Fairness and Calibration](https://mlsanity.com/paper/1709.02012/) (2017)
- [Fair Resource Allocation in Federated Learning](https://mlsanity.com/paper/1905.10497/) (2019)
- [Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment](https://mlsanity.com/paper/1610.08452/) (2016)
- [The Non-IID Data Quagmire of Decentralized Machine Learning](https://mlsanity.com/paper/1910.00189/) (2019)
- [FedML: A Research Library and Benchmark for Federated Machine Learning](https://mlsanity.com/paper/2007.13518/) (2020)
- [Fairness in Machine Learning](https://mlsanity.com/paper/2012.15816/) (2020)
- [Federated Evaluation of On-device Personalization](https://mlsanity.com/paper/1910.10252/) (2019)
- [Inherent Tradeoffs in Learning Fair Representations](https://mlsanity.com/paper/1906.08386/) (2019)
- [Federated Learning for Open Banking](https://mlsanity.com/paper/2108.10749/) (2021)
- [A Principled Approach to Data Valuation for Federated Learning](https://mlsanity.com/paper/2009.06192/) (2020)
- [FedSplit: An algorithmic framework for fast federated optimization](https://mlsanity.com/paper/2005.05238/) (2020)
- [Tilted Empirical Risk Minimization](https://mlsanity.com/paper/2007.01162/) (2020)
- [Reliable Federated Learning for Mobile Networks](https://mlsanity.com/paper/1910.06837/) (2019)
- [Federated Learning Meets Multi-objective Optimization](https://mlsanity.com/paper/2006.11489/) (2020)
- [On Tilted Losses in Machine Learning: Theory and Applications](https://mlsanity.com/paper/2109.06141/) (2021)

## Built on by (most cited first, 21 of 21)

- [Fair Resource Allocation in Multi-Task Learning](https://mlsanity.com/paper/2402.15638/) (2024)
- [Provable Pluralistic Alignment: Multi-Party RLHF under Offline Human Feedback](https://mlsanity.com/paper/2403.05006/) (2024)
- [Noise-Aware Algorithm for Heterogeneous Differentially Private Federated Learning](https://mlsanity.com/paper/2406.03519/) (2024)
- [Achieving Fairness in Multi-Agent Markov Decision Processes Using Reinforcement Learning](https://mlsanity.com/paper/2306.00324/) (2023)
- [Scaff-PD: Communication Efficient Fair and Robust Federated Learning](https://mlsanity.com/paper/2307.13381/) (2023)
- [Achieving Fairness Across Local and Global Models in Federated Learning](https://mlsanity.com/paper/2406.17102/) (2024)
- [Entropy-driven Fair and Effective Federated Learning](https://mlsanity.com/paper/2301.12407/) (2023)
- [Federated Learning Under Restricted User Availability](https://mlsanity.com/paper/2309.14176/) (2023)
- [Differentially Private Clustered Federated Learning](https://mlsanity.com/paper/2405.19272/) (2024)
- [Generalization and Robustness of the Tilted Empirical Risk](https://mlsanity.com/paper/2409.19431/) (2024)
- [Adaptive Online Mirror Descent for Tchebycheff Scalarization in Multi-Objective Learning](https://mlsanity.com/paper/2410.21764/) (2024)
- [Understanding the Role of Layer Normalization in Label-Skewed Federated Learning](https://mlsanity.com/paper/2308.09565/) (2023)
- [Pursuing Overall Welfare in Federated Learning through Sequential Decision Making](https://mlsanity.com/paper/2405.20821/) (2024)
- [Achieving Distributive Justice in Federated Learning via Uncertainty Quantification](https://mlsanity.com/paper/2504.15924/) (2025)
- [Predicting Infant Brain Connectivity with Federated Multi-Trajectory GNNs using Scarce Data](https://mlsanity.com/paper/2401.01383/) (2024)
- [Friends in Unexpected Places: Enhancing Local Fairness in Federated Learning through Clustering](https://mlsanity.com/paper/2407.19331/) (2024)
- [FedMABA: Towards Fair Federated Learning through Multi-Armed Bandits Allocation](https://mlsanity.com/paper/2410.20141/) (2024)
- [Information-Geometric Barycenters for Bayesian Federated Learning](https://mlsanity.com/paper/2412.11646/) (2024)
- [A Flexible Fairness Framework with Surrogate Loss Reweighting for Addressing Sociodemographic Disparities](https://mlsanity.com/paper/2503.16836/) (2025)
- [Cost-Free Personalization via Information-Geometric Projection in Bayesian Federated Learning](https://mlsanity.com/paper/2509.10132/) (2025)
- [FUSE: Fairness-aware Update Screening against Byzantine Exploitation in q-Fair Federated Learning](https://mlsanity.com/paper/2610.20264/) (2026)

## Abstract (arXiv)

With the increasingly broad deployment of federated learning (FL) systems in the real world, it is critical but challenging to ensure fairness in FL, i.e. reasonably satisfactory performances for each of the numerous diverse clients. In this work, we introduce and study a new fairness notion in FL, called proportional fairness (PF), which is based on the relative change of each client's performance. From its connection with the bargaining games, we propose PropFair, a novel and easy-to-implement algorithm for finding proportionally fair solutions in FL and study its convergence properties. Through extensive experiments on vision and language datasets, we demonstrate that PropFair can approximately find PF solutions, and it achieves a good balance between the average performances of all clients and of the worst 10% clients. Our code is available at urlhttps://github.com/huawei-noah/Federated-Learning/tree/main/FairFL.