Distributed Linear Bandits under Communication Constraints

by   Sudeep Salgia, et al.

We consider distributed linear bandits where M agents learn collaboratively to minimize the overall cumulative regret incurred by all agents. Information exchange is facilitated by a central server, and both the uplink and downlink communications are carried over channels with fixed capacity, which limits the amount of information that can be transmitted in each use of the channels. We investigate the regret-communication trade-off by (i) establishing information-theoretic lower bounds on the required communications (in terms of bits) for achieving a sublinear regret order; (ii) developing an efficient algorithm that achieves the minimum sublinear regret order offered by centralized learning using the minimum order of communications dictated by the information-theoretic lower bounds. For sparse linear bandits, we show a variant of the proposed algorithm offers better regret-communication trade-off by leveraging the sparsity of the problem.


page 1

page 2

page 3

page 4


Distributed Contextual Linear Bandits with Minimax Optimal Communication Cost

We study distributed contextual linear bandits with stochastic contexts,...

Information Directed Sampling for Sparse Linear Bandits

Stochastic sparse linear bandits offer a practical model for high-dimens...

Coded Distributed Computing for Hierarchical Multi-task Learning

In this paper, we consider a hierarchical distributed multi-task learnin...

Information-Theoretic Regret Bounds for Bandits with Fixed Expert Advice

We investigate the problem of bandits with expert advice when the expert...

A Communication-Efficient Adaptive Algorithm for Federated Learning under Cumulative Regret

We consider the problem of online stochastic optimization in a distribut...

Optimal Scalarizations for Sublinear Hypervolume Regret

Scalarization is a general technique that can be deployed in any multiob...

Remote Contextual Bandits

We consider a remote contextual multi-armed bandit (CMAB) problem, in wh...

Please sign up or login with your details

Forgot password? Click here to reset