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

10/15/2022
by   Qiqi Liu, et al.
0

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.

READ FULL TEXT

page 1

page 6

page 7

research
06/22/2021

A Federated Data-Driven Evolutionary Algorithm for Expensive Multi/Many-objective Optimization

Data-driven optimization has found many successful applications in the r...
research
02/16/2021

A Federated Data-Driven Evolutionary Algorithm

Data-driven evolutionary optimization has witnessed great success in sol...
research
04/06/2022

Automatic inference of fault tree models via multi-objective evolutionary algorithms

Fault tree analysis is a well-known technique in reliability engineering...
research
05/28/2022

Data-Driven Evolutionary Multi-Objective Optimization Based on Multiple-Gradient Descent for Disconnected Pareto Fronts

Data-driven evolutionary multi-objective optimization (EMO) has been rec...
research
12/14/2020

Incremental Data-driven Optimization of Complex Systems in Nonstationary Environments

Existing work on data-driven optimization focuses on problems in static ...
research
03/28/2023

Scaling Multi-Objective Security Games Provably via Space Discretization Based Evolutionary Search

In the field of security, multi-objective security games (MOSGs) allow d...

Please sign up or login with your details

Forgot password? Click here to reset