Harnessing the Power of Federated Learning in Federated Contextual Bandits

Chengshuai Shi, Kun Yang, Ruida Zhou, Cong Shen

Keywords: Federated Learning, Contextual Bandits

TL;DR: This work addresses the current disconnection between the studies of federated learning (FL) and federated contextual bandits (FCB) by proposing an innovative FCB design, which is capable of harnessing the full spectrum of FL advances.

Abstract: Federated contextual bandits (FCB), as a pivotal instance of combining federated learning (FL) and sequential decision-making, have received growing interest in recent years. However, existing FCB designs often adopt FL protocols tailored for specific settings, deviating from the canonical FL framework (e.g., the celebrated FedAvg design). Such disconnections not only prohibit these designs from flexibly leveraging canonical FL algorithmic approaches but also set considerable barriers for FCB to incorporate growing studies on FL attributes such as robustness and privacy. To promote a closer relationship between FL and FCB, we propose a novel FCB design, FedIGW, which can flexibly incorporate both existing and future FL protocols and thus is capable of harnessing the full spectrum of FL advances.

Submission Number: 2