Search Results for author:
Found 4 papers, 0 papers with code
Distributed Inference with Sparse and Quantized Communication
We prove that our rule guarantees convergence to the true state exponentially fast almost surely despite sparse communication, and that it has the potential to significantly reduce information flow from uninformative agents to informative agents.
A Communication-Efficient Algorithm for Exponentially Fast Non-Bayesian Learning in Networks
We introduce a simple time-triggered protocol to achieve communication-efficient non-Bayesian learning over a network.
A New Approach to Distributed Hypothesis Testing and Non-Bayesian Learning: Improved Learning Rate and Byzantine-Resilience
We study a setting where a group of agents, each receiving partially informative private signals, seek to collaboratively learn the true underlying state of the world (from a finite set of hypotheses) that generates their joint observation profiles.
A New Approach for Distributed Hypothesis Testing with Extensions to Byzantine-Resilience
Under minimal requirements on the signal structures of the agents and the underlying communication graph, we establish consistency of the proposed belief update rule, i. e., we show that the actual beliefs of the agents asymptotically concentrate on the true state almost surely.
