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A Secure Federated Data-Driven Evolutionary Multi-objective Optimization Algorithm by Liu, Qiqi; Yan, Yuping; Ligeti, Peter; Jin, Yaochu is a scholarly article available to read on EtoBox.

What is A Secure Federated Data-Driven Evolutionary Multi-objective Optimization Algorithm about?

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

Author

Liu, Qiqi; Yan, Yuping; Ligeti, Peter; Jin, Yaochu

Published

2022

Language

EN