A Federated Data-Driven Evolutionary Algorithm
Jinjin Xu, Yaochu Jin, Wenli Du, Sai Gu

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…
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Taxonomy
TopicsAdvanced Multi-Objective Optimization Algorithms · Metaheuristic Optimization Algorithms Research · Machine Learning and Data Classification
