# A Secure Federated Data-Driven Evolutionary Multi-objective Optimization Algorithm

arXiv: https://arxiv.org/abs/2210.08295 · HTML: https://mlsanity.com/paper/2210.08295/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 71 references that are in the map.
- First posted on arXiv 15 Oct 2022.

## Builds on (most cited first, 12 of 12)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Deep Leakage from Gradients](https://mlsanity.com/paper/1906.08935/) (2019)
- [Federated Bayesian Optimization via Thompson Sampling](https://mlsanity.com/paper/2010.10154/) (2020)
- [Recent Advances in Bayesian Optimization](https://mlsanity.com/paper/2206.03301/) (2022)
- [Differentially Private Federated Bayesian Optimization with Distributed Exploration](https://mlsanity.com/paper/2110.14153/) (2021)
- [Private Outsourced Bayesian Optimization](https://mlsanity.com/paper/2010.12799/) (2020)
- [Differentially Private Bayesian Optimization](https://mlsanity.com/paper/1501.04080/) (2015)
- [A Federated Data-Driven Evolutionary Algorithm](https://mlsanity.com/paper/2102.08288/) (2021)
- [Privacy-Preserving Gaussian Process Regression -- A Modular Approach to the Application of Homomorphic Encryption](https://mlsanity.com/paper/2001.10893/) (2020)
- [A Federated Data-Driven Evolutionary Algorithm for Expensive Multi/Many-objective Optimization](https://mlsanity.com/paper/2106.12086/) (2021)
- [Adversarial Attacks on Gaussian Process Bandits](https://mlsanity.com/paper/2110.08449/) (2021)
- [Gaussian Processes with Differential Privacy](https://mlsanity.com/paper/2106.00474/) (2021)

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

- [Optimizing Privacy, Utility and Efficiency in Constrained Multi-Objective Federated Learning](https://mlsanity.com/paper/2305.00312/) (2023)
- [Probably Approximately Correct Federated Learning](https://mlsanity.com/paper/2304.04641/) (2023)
- [Bi-level Multi-objective Evolutionary Learning: A Case Study on Multi-task Graph Neural Topology Search](https://mlsanity.com/paper/2302.02565/) (2023)
- [When Evolutionary Computation Meets Privacy](https://mlsanity.com/paper/2304.01205/) (2023)

## Abstract (arXiv)

Data-driven evolutionary algorithms usually aim to exploit the information behind a limited amount of data to perform optimization, which have proved to be successful in solving many complex real-world optimization problems. However, most data-driven evolutionary algorithms are centralized, causing privacy and security concerns. Existing federated Bayesian algorithms and data-driven evolutionary algorithms mainly protect the raw data on each client. To address this issue, this paper proposes a secure federated data-driven evolutionary multi-objective optimization algorithm to protect both the raw data and the newly infilled solutions obtained by optimizing the acquisition function conducted on the server. We select the query points on a randomly selected client at each round of surrogate update by calculating the acquisition function values of the unobserved points on this client, thereby reducing the risk of leaking the information about the solution to be sampled. In addition, since the predicted objective values of each client may contain sensitive information, we mask the objective values with Diffie-Hellmann-based noise, and then send only the masked objective values of other clients to the selected client via the server. Since the calculation of the acquisition function also requires both the predicted objective value and the uncertainty of the prediction, the predicted mean objective and uncertainty are normalized to reduce the influence of noise. Experimental results on a set of widely used multi-objective optimization benchmarks show that the proposed algorithm can protect privacy and enhance security with only negligible sacrifice in the performance of federated data-driven evolutionary optimization.