# Federated Learning as a Mean-Field Game

arXiv: https://arxiv.org/abs/2107.03770 · HTML: https://mlsanity.com/paper/2107.03770/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 9 references that are in the map.
- First posted on arXiv 8 Jul 2021.

## Builds on (most cited first, 3 of 3)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Qualitatively characterizing neural network optimization problems](https://mlsanity.com/paper/1412.6544/) (2014)
- [Neural SDE: Stabilizing Neural ODE Networks with Stochastic Noise](https://mlsanity.com/paper/1906.02355/) (2019)

## Built on by (most cited first, 4 of 4)

- [Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insights](https://mlsanity.com/paper/2312.04688/) (2023)
- [Exploring Machine Learning Models for Federated Learning: A Review of Approaches, Performance, and Limitations](https://mlsanity.com/paper/2311.10832/) (2023)
- [MEMOA: Massive Mixtures of Online Agents via Mean-Field Decentralized Nash Equilibria](https://mlsanity.com/paper/2605.05492/) (2026)
- [Multi-Agent Privacy Game in Federated Learning: A Unified Mean-Field View](https://mlsanity.com/paper/2607.23029/) (2026)

## Abstract (arXiv)

We establish a connection between federated learning, a concept from machine learning, and mean-field games, a concept from game theory and control theory. In this analogy, the local federated learners are considered as the players and the aggregation of the gradients in a central server is the mean-field effect. We present federated learning as a differential game and discuss the properties of the equilibrium of this game. We hope this novel view to federated learning brings together researchers from these two distinct areas to work on fundamental problems of large-scale distributed and privacy-preserving learning algorithms.