# Federated Learning Meets Multi-objective Optimization

arXiv: https://arxiv.org/abs/2006.11489 · HTML: https://mlsanity.com/paper/2006.11489/

## Key facts

- Cited by 53 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jun 2020.

## Built on by (most cited first, 30 of 53)

- [A Field Guide to Federated Optimization](https://mlsanity.com/paper/2107.06917/) (2021)
- [From Distributed Machine Learning to Federated Learning: A Survey](https://mlsanity.com/paper/2104.14362/) (2021)
- [Federated Learning with Fair Averaging](https://mlsanity.com/paper/2104.14937/) (2021)
- [Towards Fairness-Aware Federated Learning](https://mlsanity.com/paper/2111.01872/) (2021)
- [Fair Federated Medical Image Segmentation via Client Contribution Estimation](https://mlsanity.com/paper/2303.16520/) (2023)
- [Recent Advances on Federated Learning: A Systematic Survey](https://mlsanity.com/paper/2301.01299/) (2023)
- [Enforcing fairness in private federated learning via the modified method of differential multipliers](https://mlsanity.com/paper/2109.08604/) (2021)
- [Proportional Fairness in Federated Learning](https://mlsanity.com/paper/2202.01666/) (2022)
- [One-shot Federated Learning without Server-side Training](https://mlsanity.com/paper/2204.12493/) (2022)
- [Gradient-Based Multi-Objective Deep Learning: Algorithms, Theories, Applications, and Beyond](https://mlsanity.com/paper/2501.10945/) (2025)
- [FLShield: A Validation Based Federated Learning Framework to Defend Against Poisoning Attacks](https://mlsanity.com/paper/2308.05832/) (2023)
- [AdaFed: Fair Federated Learning via Adaptive Common Descent Direction](https://mlsanity.com/paper/2401.04993/) (2024)
- [Rethinking the Representation in Federated Unsupervised Learning with Non-IID Data](https://mlsanity.com/paper/2403.16398/) (2024)
- [Optimizing Privacy, Utility and Efficiency in Constrained Multi-Objective Federated Learning](https://mlsanity.com/paper/2305.00312/) (2023)
- [Federated Multi-Objective Learning](https://mlsanity.com/paper/2310.09866/) (2023)
- [FedProK: Trustworthy Federated Class-Incremental Learning via Prototypical Feature Knowledge Transfer](https://mlsanity.com/paper/2405.02685/) (2024)
- [Accelerating Fair Federated Learning: Adaptive Federated Adam](https://mlsanity.com/paper/2301.09357/) (2023)
- [Federated Learning with Domain Shift Eraser](https://mlsanity.com/paper/2503.13063/) (2025)
- [Many-Objective Multi-Solution Transport](https://mlsanity.com/paper/2403.04099/) (2024)
- [Distributed Quasi-Newton Method for Fair and Fast Federated Learning](https://mlsanity.com/paper/2501.10877/) (2025)
- [When Federated Learning Meets Quantum Computing: Survey and Research Opportunities](https://mlsanity.com/paper/2504.08814/) (2025)
- [Entropy-driven Fair and Effective Federated Learning](https://mlsanity.com/paper/2301.12407/) (2023)
- [Adaptive Online Mirror Descent for Tchebycheff Scalarization in Multi-Objective Learning](https://mlsanity.com/paper/2410.21764/) (2024)
- [Bi-level Multi-objective Evolutionary Learning: A Case Study on Multi-task Graph Neural Topology Search](https://mlsanity.com/paper/2302.02565/) (2023)
- [Federated Communication-Efficient Multi-Objective Optimization](https://mlsanity.com/paper/2410.16398/) (2024)
- [FedDP: Privacy-preserving method based on federated learning for histopathology image segmentation](https://mlsanity.com/paper/2411.04509/) (2024)
- [Over-the-Air Fair Federated Learning via Multi-Objective Optimization](https://mlsanity.com/paper/2501.03392/) (2025)
- [Multi-objective methods in Federated Learning: A survey and taxonomy](https://mlsanity.com/paper/2502.03108/) (2025)
- [Federated Fairness without Access to Sensitive Groups](https://mlsanity.com/paper/2402.14929/) (2024)
- [Pursuing Overall Welfare in Federated Learning through Sequential Decision Making](https://mlsanity.com/paper/2405.20821/) (2024)

## Abstract (arXiv)

Federated learning has emerged as a promising, massively distributed way to train a joint deep model over large amounts of edge devices while keeping private user data strictly on device. In this work, motivated from ensuring fairness among users and robustness against malicious adversaries, we formulate federated learning as multi-objective optimization and propose a new algorithm FedMGDA+ that is guaranteed to converge to Pareto stationary solutions. FedMGDA+ is simple to implement, has fewer hyperparameters to tune, and refrains from sacrificing the performance of any participating user. We establish the convergence properties of FedMGDA+ and point out its connections to existing approaches. Extensive experiments on a variety of datasets confirm that FedMGDA+ compares favorably against state-of-the-art.