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.
翻译:由数据驱动的进化算法通常旨在利用数量有限的数据背后的信息来进行优化,这已证明成功地解决了许多复杂的现实世界优化问题。然而,大多数由数据驱动的进化算法都是集中的,引起隐私和安全关切。现有的联盟式贝叶西亚算法和数据驱动的进化算法主要保护每个客户的原始数据。为解决这一问题,本文件建议采用一种安全的由数据驱动的、由数据驱动的进化多目标优化算法,以保护原始数据,并通过优化服务器的获取功能而获得的新填充的解决方案。我们在每轮代孕更新中随机选择的客户的查询点,方法是计算该客户未观察到的点的获取功能值,从而减少泄露有关解决办法的信息的风险。此外,由于每个客户的预测目标值可能包含敏感信息,我们用Diffie-Hellmann的噪音来掩盖目标值,然后通过服务器将其他客户的蒙蔽的客观值发送给选定的客户。由于计算采购职能的每轮更新,计算出该客户未观察到的购买值的购买功能值值值,因此,还需要广泛预测用于递增缩的精确度的精确度,同时显示用于预测的精确度的精确度的精确度。