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Poster
in
Workshop: Multi-Agent Security: Security as Key to AI Safety

Harnessing the Power of Federated Learning in Federated Contextual Bandits

Chengshuai Shi · Kun Yang · Ruida Zhou · Cong Shen

Keywords: [ contextual bandits ] [ federated learning ]


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. 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.

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