Federated Bayesian Optimization via Thompson Sampling

Understand

Bayesian optimization (BO) is a prominent approach to optimizing expensive-to-evaluate black-box functions.

• The massive computational capability of edge devices such as mobile phones, coupled with privacy concerns, has led to a surging interest in federated learning (FL) which focuses on collaborative training of deep neural networks (DNNs) via first-order optimization techniques.
• However, some common machine learning tasks such as hyperparameter tuning of DNNs lack access to gradients and thus require zeroth-order/black-box optimization.
• This hints at the possibility of extending BO to the FL setting (FBO) for agents to collaborate in these black-box optimization tasks.