  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.
