Federated Online Sparse Decision Making
This addresses efficient decision-making in distributed systems for applications like personalized recommendations, though it is incremental in combining federated learning with bandit methods.
The paper tackles the problem of federated linear contextual bandits with high-dimensional contexts and heterogeneous clients, proposing Fedego Lasso to achieve near-optimal regrets with logarithmic communication costs.
This paper presents a novel federated linear contextual bandits model, where individual clients face different K-armed stochastic bandits with high-dimensional decision context and coupled through common global parameters. By leveraging the sparsity structure of the linear reward , a collaborative algorithm named \texttt{Fedego Lasso} is proposed to cope with the heterogeneity across clients without exchanging local decision context vectors or raw reward data. \texttt{Fedego Lasso} relies on a novel multi-client teamwork-selfish bandit policy design, and achieves near-optimal regrets for shared parameter cases with logarithmic communication costs. In addition, a new conceptual tool called federated-egocentric policies is introduced to delineate exploration-exploitation trade-off. Experiments demonstrate the effectiveness of the proposed algorithms on both synthetic and real-world datasets.