# A Federated Data-Driven Evolutionary Algorithm

arXiv: https://arxiv.org/abs/2102.08288 · HTML: https://mlsanity.com/paper/2102.08288/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 81 references that are in the map.
- First posted on arXiv 16 Feb 2021.

## Builds on (most cited first, 22 of 22)

- [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)
- [Federated Learning with Non-IID Data](https://mlsanity.com/paper/1806.00582/) (2018)
- [Towards Federated Learning at Scale: System Design](https://mlsanity.com/paper/1902.01046/) (2019)
- [Federated Optimization: Distributed Machine Learning for On-Device Intelligence](https://mlsanity.com/paper/1610.02527/) (2016)
- [Federated Learning with Differential Privacy: Algorithms and Performance Analysis](https://mlsanity.com/paper/1911.00222/) (2019)
- [Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent](https://mlsanity.com/paper/1705.09056/) (2017)
- [Agnostic Federated Learning](https://mlsanity.com/paper/1902.00146/) (2019)
- [Robust and Communication-Efficient Federated Learning from Non-IID Data](https://mlsanity.com/paper/1903.02891/) (2019)
- [Federated Machine Learning: Concept and Applications](https://mlsanity.com/paper/1902.04885/) (2019)
- [Adaptive Federated Learning in Resource Constrained Edge Computing Systems](https://mlsanity.com/paper/1804.05271/) (2018)
- [Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge](https://mlsanity.com/paper/1804.08333/) (2018)
- [EXTRA: An Exact First-Order Algorithm for Decentralized Consensus Optimization](https://mlsanity.com/paper/1404.6264/) (2014)
- [Communication-Efficient Federated Deep Learning with Asynchronous Model Update and Temporally Weighted Aggregation](https://mlsanity.com/paper/1903.07424/) (2019)
- [VAFL: a Method of Vertical Asynchronous Federated Learning](https://mlsanity.com/paper/2007.06081/) (2020)
- [Federated Learning with Bayesian Differential Privacy](https://mlsanity.com/paper/1911.10071/) (2019)
- [Ternary Compression for Communication-Efficient Federated Learning](https://mlsanity.com/paper/2003.03564/) (2020)
- [Multi-objective Evolutionary Federated Learning](https://mlsanity.com/paper/1812.07478/) (2018)
- [From Federated Learning to Federated Neural Architecture Search: A Survey](https://mlsanity.com/paper/2009.05868/) (2020)
- [Distributed Additive Encryption and Quantization for Privacy Preserving Federated Deep Learning](https://mlsanity.com/paper/2011.12623/) (2020)
- [AsySPA: An Exact Asynchronous Algorithm for Convex Optimization Over Digraphs](https://mlsanity.com/paper/1808.04118/) (2018)
- [Surrogate Model Assisted Cooperative Coevolution for Large Scale Optimization](https://mlsanity.com/paper/1802.09746/) (2018)

## Built on by (most cited first, 8 of 8)

- [Federated Learning on Non-IID Data: A Survey](https://mlsanity.com/paper/2106.06843/) (2021)
- [Recent Advances in Bayesian Optimization](https://mlsanity.com/paper/2206.03301/) (2022)
- [Optimizing Privacy, Utility and Efficiency in Constrained Multi-Objective Federated Learning](https://mlsanity.com/paper/2305.00312/) (2023)
- [A Federated Data-Driven Evolutionary Algorithm for Expensive Multi/Many-objective Optimization](https://mlsanity.com/paper/2106.12086/) (2021)
- [Towards Fairness-Aware Multi-Objective Optimization](https://mlsanity.com/paper/2207.12138/) (2022)
- [A Secure Federated Data-Driven Evolutionary Multi-objective Optimization Algorithm](https://mlsanity.com/paper/2210.08295/) (2022)
- [Probably Approximately Correct Federated Learning](https://mlsanity.com/paper/2304.04641/) (2023)
- [Optimizing Algorithms From Pairwise User Preferences](https://mlsanity.com/paper/2308.04571/) (2023)

## Abstract (arXiv)

Data-driven evolutionary optimization has witnessed great success in solving complex real-world optimization problems. However, existing data-driven optimization algorithms require that all data are centrally stored, which is not always practical and may be vulnerable to privacy leakage and security threats if the data must be collected from different devices. To address the above issue, this paper proposes a federated data-driven evolutionary optimization framework that is able to perform data driven optimization when the data is distributed on multiple devices. On the basis of federated learning, a sorted model aggregation method is developed for aggregating local surrogates based on radial-basis-function networks. In addition, a federated surrogate management strategy is suggested by designing an acquisition function that takes into account the information of both the global and local surrogate models. Empirical studies on a set of widely used benchmark functions in the presence of various data distributions demonstrate the effectiveness of the proposed framework.