Distributed Learning: Sequential Decision Making in Resource-Constrained Environments
This work addresses communication efficiency for distributed systems in resource-constrained environments, offering a novel protocol that reduces costs while maintaining performance.
The paper tackles the problem of high communication costs in distributed learning for sequential decision making by proposing a partial communication protocol, achieving the same performance order as full communication but with a significantly reduced cost of O(log T) compared to O(T).
We study cost-effective communication strategies that can be used to improve the performance of distributed learning systems in resource-constrained environments. For distributed learning in sequential decision making, we propose a new cost-effective partial communication protocol. We illustrate that with this protocol the group obtains the same order of performance that it obtains with full communication. Moreover, we prove that under the proposed partial communication protocol the communication cost is $O(\log T)$, where $T$ is the time horizon of the decision-making process. This improves significantly on protocols with full communication, which incur a communication cost that is $O(T)$. We validate our theoretical results using numerical simulations.