Model-output-based federated Bayesian optimization

Abstract

Abstract Bayesian optimization (BO) has evolved from single-agent optimization to multi-agent collaborative optimization, namely Federated Bayesian Optimization (FBO), aimed at collaboratively improving the optimization performance of all agents. Due to the limited raw data contained by each agent and privacy concerns, it is very challenging for existing FBO to perform collaborative optimization. In this work, we propose an innovative FBO method that suggests transmitting the predicted values of surrogate models between the agent and the server and aggregating the weighted model output based on the similarity of the agent to address privacy concerns. The similarity between agents is measured according to the predictions of the agents’ surrogate model. Additionally, we propose augmenting the limited raw data problem by adopting the generative adversarial network to generate a set of solutions with global information to improve the effectiveness of model management. This model output-based FBO method demonstrates competitiveness in both benchmark and real-world problems while guaranteeing privacy protection.