  This paper addresses the problem of non-Bayesian learning over multi-agent
networks, where agents repeatedly collect partially informative observations
about an unknown state of the world, and try to collaboratively learn the true
state. We focus on the impact of the adversarial agents on the performance of
consensus-based non-Bayesian learning, where non-faulty agents combine local
learning updates with consensus primitives. In particular, we consider the
scenario where an unknown subset of agents suffer Byzantine faults -- agents
suffering Byzantine faults behave arbitrarily. Two different learning rules are
proposed.
