  There is a rapid increase in the cooperative learning paradigm in online
learning settings, i.e., federated learning (FL). Unlike most FL settings,
there are many situations where the agents are competitive. Each agent would
like to learn from others, but the part of the information it shares for others
to learn from could be sensitive; thus, it desires its privacy. This work
investigates a group of agents working concurrently to solve similar
combinatorial bandit problems while maintaining quality constraints. Can these
agents collectively learn while keeping their sensitive information
confidential by employing differential privacy? We observe that communicating
can reduce the regret. However, differential privacy techniques for protecting
sensitive information makes the data noisy and may deteriorate than help to
improve regret. Hence, we note that it is essential to decide when to
communicate and what shared data to learn to strike a functional balance
between regret and privacy. For such a federated combinatorial MAB setting, we
propose a Privacy-preserving Federated Combinatorial Bandit algorithm, P-FCB.
We illustrate the efficacy of P-FCB through simulations. We further show that
our algorithm provides an improvement in terms of regret while upholding
quality threshold and meaningful privacy guarantees.
