  We study the problem of distributed hypothesis testing over a network of
mobile agents with limited communication and sensing ranges to infer the true
hypothesis collaboratively. In particular, we consider a scenario where there
is an unknown subset of compromised agents that may deliberately share altered
information to undermine the team objective. We propose two distributed
algorithms where each agent maintains and updates two sets of beliefs (i.e.,
probability distributions over the hypotheses), namely local and actual beliefs
(LB and AB respectively for brevity). In both algorithms, at every time step,
each agent shares its AB with other agents within its communication range and
makes a local observation to update its LB. Then both algorithms can use the
shared information to update ABs under certain conditions. One requires
receiving a certain number of shared ABs at each time instant; the other
accumulates shared ABs over time and updates after the number of shared ABs
exceeds a prescribed threshold. Otherwise, both algorithms rely on the agent's
current LB and AB to update the new AB. We prove under mild assumptions that
the AB for every non-compromised agent converges almost surely to the true
hypothesis, without requiring connectivity in the underlying time-varying
network topology. Using a simulation of a team of unmanned aerial vehicles
aiming to classify adversarial agents among themselves, we illustrate and
compare the proposed algorithms. Finally, we show experimentally that the
second algorithm consistently outperforms the first algorithm in terms of the
speed of convergence.
