A Federated Data-Driven Evolutionary Algorithm

TL;DR

This paper introduces a federated data-driven evolutionary optimization framework that enables privacy-preserving optimization across multiple devices using federated learning and surrogate models, demonstrating effectiveness on benchmark functions.

Contribution

It proposes a novel federated learning-based framework with surrogate aggregation and management strategies for distributed data-driven optimization.

Findings

Effective in handling distributed data with various distributions

Outperforms traditional centralized methods in privacy-preserving scenarios

Shows promising results on benchmark optimization problems

Abstract

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.