Vertical Federated Linear Contextual Bandits
This work addresses privacy-preserving online recommendation for distributed departments, presenting a novel approach in an unexplored area, though it is incremental as it adapts existing bandit algorithms with a new encryption method.
The paper tackles the problem of building contextual bandits in a vertical federated setting, where contextual information is distributed across departments, by designing a customized encryption scheme (O3M) and applying it to LinUCB and LinTS algorithms, achieving perfect recovery of centralized service quality with satisfactory runtime efficiency as proven theoretically and validated on synthetic and real-world datasets.
In this paper, we investigate a novel problem of building contextual bandits in the vertical federated setting, i.e., contextual information is vertically distributed over different departments. This problem remains largely unexplored in the research community. To this end, we carefully design a customized encryption scheme named orthogonal matrix-based mask mechanism(O3M) for encrypting local contextual information while avoiding expensive conventional cryptographic techniques. We further apply the mechanism to two commonly-used bandit algorithms, LinUCB and LinTS, and instantiate two practical protocols for online recommendation under the vertical federated setting. The proposed protocols can perfectly recover the service quality of centralized bandit algorithms while achieving a satisfactory runtime efficiency, which is theoretically proved and analyzed in this paper. By conducting extensive experiments on both synthetic and real-world datasets, we show the superiority of the proposed method in terms of privacy protection and recommendation performance.