Federated Contextual Cascading Bandits with Asynchronous Communication
  and Heterogeneous Users
Hantao Yang Xutong Liu Zhiyong Wang Hong Xie John C. S. Lui Defu Lian Enhong Chen
We study the problem of federated contextual combinatorial cascading bandits,
where $|\mathcal{U}|$ agents collaborate under the coordination of a central
server to provide tailored recommendations to the $|\mathcal{U}|$ corresponding
users. Existing works consider either a synchronous framework, necessitating
full agent participation and global synchronization, or assume user homogeneity
with identical behaviors. We overcome these limitations by considering (1)
federated agents operating in an asynchronous communication paradigm, where no
mandatory synchronization is required and all agents communicate independently
with the server, (2) heterogeneous user behaviors, where users can be
stratified into $J \le |\mathcal{U}|$ latent user clusters, each exhibiting
distinct preferences. For this setting, we propose a UCB-type algorithm with
delicate communication protocols. Through theoretical analysis, we give
sub-linear regret bounds on par with those achieved in the synchronous
framework, while incurring only logarithmic communication costs. Empirical
evaluation on synthetic and real-world datasets validates our algorithm's
superior performance in terms of regrets and communication costs.