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We study a setting where a group of agents, each receiving partiallyinformative private signals, seek to collaboratively learn the true underlyingstate of the world (from a finite set of hypotheses) that generates their jointobservation profiles. To solve this problem, we propose a distributed learningrule that differs fundamentally from existing approaches, in that it does notemploy any form of "belief-averaging". Instead, agents update their beliefsbased on a min-rule. Under standard assumptions on the observation model andthe network structure, we establish that each agent learns the truthasymptotically almost surely. As our main contribution, we prove that withprobability 1, each false hypothesis is ruled out by every agent exponentiallyfast at a network-independent rate that is strictly larger than existing rates.We then develop a computationally-efficient variant of our learning rule thatis provably resilient to agents who do not behave as expected (as representedby a Byzantine adversary model) and deliberately try to spread misinformation.

A New Approach to Distributed Hypothesis Testing and Non-Bayesian Learning: Improved Learning Rate and Byzantine-Resilience