Federated Bandit

Federated bandit learning addresses the challenge of collaboratively solving bandit problems across multiple agents while preserving data privacy and minimizing communication overhead. Current research focuses on extending bandit algorithms (like Thompson Sampling and UCB) to federated settings, handling heterogeneous clients and non-linear reward functions, and incorporating incentives to encourage participation. This field is significant because it enables efficient and privacy-preserving machine learning in distributed environments, with applications ranging from personalized recommendations to resource allocation in large-scale systems. Addressing challenges like Byzantine attacks and asynchronous communication remains an active area of investigation.