# Anti-Matthew FL: Bridging the Performance Gap in Federated Learning to Counteract the Matthew Effect

arXiv: https://arxiv.org/abs/2309.16338 · HTML: https://mlsanity.com/paper/2309.16338/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 30 references that are in the map.
- First posted on arXiv 28 Sep 2023.

## Builds on (most cited first, 23 of 23)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Ditto: Fair and Robust Federated Learning Through Personalization](https://mlsanity.com/paper/2012.04221/) (2020)
- [Agnostic Federated Learning](https://mlsanity.com/paper/1902.00146/) (2019)
- [Retiring Adult: New Datasets for Fair Machine Learning](https://mlsanity.com/paper/2108.04884/) (2021)
- [Fair Resource Allocation in Federated Learning](https://mlsanity.com/paper/1905.10497/) (2019)
- [Pareto Multi-Task Learning](https://mlsanity.com/paper/1912.12854/) (2019)
- [Federated Learning for Smart Healthcare: A Survey](https://mlsanity.com/paper/2111.08834/) (2021)
- [Federated Learning for Open Banking](https://mlsanity.com/paper/2108.10749/) (2021)
- [FairFed: Enabling Group Fairness in Federated Learning](https://mlsanity.com/paper/2110.00857/) (2021)
- [FairBatch: Batch Selection for Model Fairness](https://mlsanity.com/paper/2012.01696/) (2020)
- [Fairness-aware Agnostic Federated Learning](https://mlsanity.com/paper/2010.05057/) (2020)
- [Addressing Algorithmic Disparity and Performance Inconsistency in Federated Learning](https://mlsanity.com/paper/2108.08435/) (2021)
- [Understanding and Improving Fairness-Accuracy Trade-offs in Multi-Task Learning](https://mlsanity.com/paper/2106.02705/) (2021)
- [Training Fair Models in Federated Learning without Data Privacy Infringement](https://mlsanity.com/paper/2109.05662/) (2021)
- [Minimax Demographic Group Fairness in Federated Learning](https://mlsanity.com/paper/2201.08304/) (2022)
- [Group Fairness by Probabilistic Modeling with Latent Fair Decisions](https://mlsanity.com/paper/2009.09031/) (2020)
- [Incentivizing Collaboration in Machine Learning via Synthetic Data Rewards](https://mlsanity.com/paper/2112.09327/) (2021)
- [Towards Understanding Fairness and its Composition in Ensemble Machine Learning](https://mlsanity.com/paper/2212.04593/) (2022)
- [Improving Fairness in AI Models on Electronic Health Records: The Case for Federated Learning Methods](https://mlsanity.com/paper/2305.11386/) (2023)
- [Fair Representation: Guaranteeing Approximate Multiple Group Fairness for Unknown Tasks](https://mlsanity.com/paper/2109.00545/) (2021)
- [Fair Federated Learning via Bounded Group Loss](https://mlsanity.com/paper/2203.10190/) (2022)
- [Generalizability of predictive models for intensive care unit patients](https://mlsanity.com/paper/1812.02275/) (2018)
- [Matchings with Group Fairness Constraints: Online and Offline Algorithms](https://mlsanity.com/paper/2105.09522/) (2021)

## Abstract (arXiv)

Federated learning (FL) stands as a paradigmatic approach that facilitates model training across heterogeneous and diverse datasets originating from various data providers. However, conventional FLs fall short of achieving consistent performance, potentially leading to performance degradation for clients who are disadvantaged in data resources. Influenced by the Matthew effect, deploying a performance-imbalanced global model in applications further impedes the generation of high-quality data from disadvantaged clients, exacerbating the disparities in data resources among clients. In this work, we propose anti-Matthew fairness for the global model at the client level, requiring equal accuracy and equal decision bias across clients. To balance the trade-off between achieving anti-Matthew fairness and performance optimality, we formalize the anti-Matthew effect federated learning (anti-Matthew FL) as a multi-constrained multi-objectives optimization (MCMOO) problem and propose a three-stage multi-gradient descent algorithm to obtain the Pareto optimality. We theoretically analyze the convergence and time complexity of our proposed algorithms. Additionally, through extensive experimentation, we demonstrate that our proposed anti-Matthew FL outperforms other state-of-the-art FL algorithms in achieving a high-performance global model while effectively bridging performance gaps among clients. We hope this work provides valuable insights into the manifestation of the Matthew effect in FL and other decentralized learning scenarios and can contribute to designing fairer learning mechanisms, ultimately fostering societal welfare.