  Federated learning (FL) has demonstrated great potential in revolutionizing
distributed machine learning, and tremendous efforts have been made to extend
it beyond the original focus on supervised learning. Among many directions,
federated contextual bandits (FCB), a pivotal integration of FL and sequential
decision-making, has garnered significant attention in recent years. Despite
substantial progress, existing FCB approaches have largely employed their
tailored FL components, often deviating from the canonical FL framework.
Consequently, even renowned algorithms like FedAvg remain under-utilized in
FCB, let alone other FL advancements. Motivated by this disconnection, this
work takes one step towards building a tighter relationship between the
canonical FL study and the investigations on FCB. In particular, a novel FCB
design, termed FedIGW, is proposed to leverage a regression-based CB algorithm,
i.e., inverse gap weighting. Compared with existing FCB approaches, the
proposed FedIGW design can better harness the entire spectrum of FL
innovations, which is concretely reflected as (1) flexible incorporation of
(both existing and forthcoming) FL protocols; (2) modularized plug-in of FL
analyses in performance guarantees; (3) seamless integration of FL appendages
(such as personalization, robustness, and privacy). We substantiate these
claims through rigorous theoretical analyses and empirical evaluations.
