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.

翻译：联邦学习在革新分布式机器学习方面展现出巨大潜力，学界已投入大量精力将其拓展至监督学习以外的领域。其中，联邦情境赌博机作为联邦学习与序列决策的关键融合方向，近年来备受关注。尽管已取得显著进展，现有联邦情境赌博机方法大多采用定制化的联邦学习组件，往往偏离标准联邦学习框架。这导致即便如FedAvg等著名算法在联邦情境赌博机中也未得到充分应用，更遑论其他联邦学习前沿成果。针对这一割裂现状，本研究致力于强化标准联邦学习研究与联邦情境赌博机探索之间的紧密关联。具体而言，我们提出名为FedIGW的新型联邦情境赌博机架构，通过采用基于回归的情境赌博机算法——逆间隙加权。与现有联邦情境赌博机方法相比，FedIGW设计能更充分地利用联邦学习创新成果的全谱系优势，具体体现在：（1）灵活整合现有及未来联邦学习协议；（2）在性能保障中模块化接入联邦学习分析；（3）无缝集成联邦学习附属功能（如个性化、鲁棒性与隐私保护）。我们通过严谨的理论分析与实证评估验证了上述论断。