A Secure Federated Data-Driven Evolutionary Multi-objective Optimization Algorithm
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Evolutionary AlgorithmsSimilar Papers 제목 키워드 기반
A Federated Data-Driven Evolutionary Algorithm for Expensive Multi/Many-objective Optimization
Data-driven optimization has found many successful applications in the real world and received increased attention in the field of evolutionary optimization. Most existing algorithms assume that the data used for optimiz…
Evolutionary AlgorithmsFederated LearningA Federated Data-Driven Evolutionary Algorithm
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, whi…
Federated LearningManagementSecure Multi-Modal Data Fusion in Federated Digital Health Systems via MCP
Secure and interoperable integration of heterogeneous medical data remains a grand challenge in digital health. Current federated learning (FL) frameworks offer privacy-preserving model training but lack standardized mec…
Federated LearningBF-Meta: Secure Blockchain-enhanced Privacy-preserving Federated Learning for Metaverse
The metaverse, emerging as a revolutionary platform for social and economic activities, provides various virtual services while posing security and privacy challenges. Wearable devices serve as bridges between the real w…
Federated LearningPrivacy PreservingAn advanced data fabric architecture leveraging homomorphic encryption and federated learning
Data fabric is an automated and AI-driven data fusion approach to accomplish data management unification without moving data to a centralized location for solving complex data problems. In a Federated learning architectu…
Federated LearningMedical Image AnalysisPrivacy Preserving