# Communication Efficient and Privacy-Preserving Federated Learning Based on Evolution Strategies

arXiv: https://arxiv.org/abs/2311.03405 · HTML: https://mlsanity.com/paper/2311.03405/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 28 references that are in the map.
- First posted on arXiv 5 Nov 2023.

## Builds on (most cited first, 16 of 16)

- [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)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization](https://mlsanity.com/paper/2007.07481/) (2020)
- [Evolution Strategies as a Scalable Alternative to Reinforcement Learning](https://mlsanity.com/paper/1703.03864/) (2017)
- [Federated Learning with Differential Privacy: Algorithms and Performance Analysis](https://mlsanity.com/paper/1911.00222/) (2019)
- [The CMA Evolution Strategy: A Tutorial](https://mlsanity.com/paper/1604.00772/) (2016)
- [A Hybrid Approach to Privacy-Preserving Federated Learning](https://mlsanity.com/paper/1812.03224/) (2018)
- [Evolution-Guided Policy Gradient in Reinforcement Learning](https://mlsanity.com/paper/1805.07917/) (2018)
- [Communication-Efficient Stochastic Zeroth-Order Optimization for Federated Learning](https://mlsanity.com/paper/2201.09531/) (2022)
- [Unbiased Gradient Estimation in Unrolled Computation Graphs with Persistent Evolution Strategies](https://mlsanity.com/paper/2112.13835/) (2021)
- [Federated Stochastic Approximation under Markov Noise and Heterogeneity: Applications in Reinforcement Learning](https://mlsanity.com/paper/2206.10185/) (2022)
- [Improved Communication Efficiency in Federated Natural Policy Gradient via ADMM-based Gradient Updates](https://mlsanity.com/paper/2310.19807/) (2023)
- [Zeroth Order Nonconvex Multi-Agent Optimization over Networks](https://mlsanity.com/paper/1710.09997/) (2017)
- [Zeroth-Order Algorithms for Stochastic Distributed Nonconvex Optimization](https://mlsanity.com/paper/2106.02958/) (2021)

## Abstract (arXiv)

Federated learning (FL) is an emerging paradigm for training deep neural networks (DNNs) in distributed manners. Current FL approaches all suffer from high communication overhead and information leakage. In this work, we present a federated learning algorithm based on evolution strategies (FedES), a zeroth-order training method. Instead of transmitting model parameters, FedES only communicates loss values, and thus has very low communication overhead. Moreover, a third party is unable to estimate gradients without knowing the pre-shared seed, which protects data privacy. Experimental results demonstrate FedES can achieve the above benefits while keeping convergence performance the same as that with back propagation methods.