PUB - Publikationen an der Universität Bielefeld

In surrogate-assisted evolutionary optimization, privacy-preservation and trusted data sharing has become an increasingly important concern, especially in scenarios involving distributed sensitive data. Existing privacy-preserving surrogate-assisted evolutionary optimization algorithms heavily rely on the basic federated learning framework. However, recent findings have revealed possible vulnerabilities within this framework, including susceptibility to adversarial threats like gradient leakage and inference attacks. To address the above challenges and enhance privacy protection, this paper proposes to protect the raw data by applying a differentially private stochastic gradient descent method to train surrogate models. A differential evolution operator is designed to generate personalized new samples for multiple clients based on promising and additional auxiliary samples, avoiding the exposure of online newly generated data. Moreover, a similarity-based aggregation algorithm is integrated to effectively construct the global surrogate model. A rigorous security analysis is provided to further validate the effectiveness of the proposed method in privacy protection. Experimental results show that the proposed method exhibits remarkable optimization performance on a set of synthetic problems with federated settings while maintaining the data privacy.