Asynchronous Distributed Hypothesis Testing in the Presence of Crash Failures

Understand

This paper addresses the problem of distributed hypothesis testing in multi-agent networks, where agents repeatedly collect local observations about an unknown state of the world, and try to collaboratively detect the true state through information exchange.

• We focus on the impact of failures and asynchrony (two fundamental factors in distributed systems) on the performance of consensus-based non-Bayesian learning.
• In particular, we consider the scenario where the networked agents may suffer crash faults, and messages delay can be arbitrarily long but finite.
• We identify the minimal global detectability of the network for non-Bayesian rule to succeed.