Efficient Knowledge Transfer in Federated Bayesian Optimization through Neural Network Surrogates

Federated Bayesian Optimization (FedBO) has emerged as a powerful paradigm for enhancing Bayesian Optimization (BO) in distributed, privacy-sensitive settings, enabling agents to accelerate the optimization of their local objectives by sharing knowledge about their similarities without exposing raw data. However, existing methods rely on restrictive assumptions, such as near-identical objective functions or shared Gaussian process hyperparameters that are often violated in heterogeneous real-world applications. We present GLOBAFED, a novel framework that enables efficient knowledge transfer across different but related objectives without compromising privacy. At its core is a probabilistic neural network surrogate with a shared feature-extracting backbone and agent-specific heads equipped with variational Bayesian last layers to provide uncertainty estimates. During optimization, agents use federated learning on the shared backbone to collaboratively build a common latent feature representation, while agent-specific heads adapt to their individual objectives. Our empirical results demonstrate that GLOBAFED significantly accelerates convergence and reaches better final performance compared to state-of-the-art FedBO baselines, and enables agents to warm-start their optimization from previously learned global features.