Distributed Bayesian Optimization with Federated Learning

Abstract

This paper proposes a novel distributed Bayesian optimization framework utilizing federated learning to optimize black-box functions across multiple clients while preserving data privacy. The core concept involves each client independently training a local Bayesian model based on its local dataset. Subsequently, a central server orchestrates the optimization process by aggregating these local models through a federated averaging algorithm, thereby updating a global Bayesian model. This approach eliminates the need for direct data sharing, a critical advantage in scenarios where data privacy is paramount. We demonstrate the efficacy of this framework, highlighting its potential for applications in areas such as hyperparameter tuning, robotics, and drug discovery, where data is often distributed and sensitive. The theoretical foundation rests on Bayesian optimization principles and the established methodologies of federated learning, creating a robust and adaptable solution. This work contributes to the growing field of privacy-preserving optimization.