Optimization of an Implicit Acquisition Function for Federated Bayesian Many-Task Optimization

Many-task optimization typically assumes that all data is available on a single device without taking into account privacy concerns. Federated many-task optimization, which involves using data from multiple devices, faces challenges due to the need to balance between knowledge sharing and privacy protection. To tackle the above challenge, we introduce a federated Bayesian optimization algorithm that optimizes the global acquisition function, which is approximated through a global classifier constructed by aggregating the local classifiers from all clients. In the proposed algorithm, a local neural network classifier is constructed on each client to learn the pairwise rank relationship between two solutions, which is trained on samples generated by the local acquisition function. Then, the parameters of local classifiers are transmitted to the server to construct a global classifier, based on which a competitive particle swarm optimizer is employed to optimize the global acquisition function without explicitly building it. In this way, the proposed algorithm can perform optimization using data distributed on multiple clients without sharing them. To more effectively deal with many-task optimization problems, the similarity between the tasks is measured according to the Euclidean distance between the weight vectors of the local classifiers, such that clients having similar tasks will share a common global classifier. To validate the performance of the proposed algorithm, we conduct empirical studies on a set of single-task and many-task benchmark problems. The experimental results demonstrate that our algorithm is highly competitive, showcasing its efficiency and effectiveness compared with the state-of-the-art privacy-preserving Bayesian optimization algorithms.