  We study the problem of resilient average consensus in multi-agent systems
where some of the agents are subject to failures or attacks. The objective of
resilient average consensus is for non-faulty/normal agents to converge to the
average of their initial values despite the erroneous effects from malicious
agents. To this end, we propose a successful distributed iterative resilient
average consensus algorithm for the multi-agent networks with general directed
topologies. The proposed algorithm has two parts at each iteration: detection
and averaging. For the detection part, we propose two distributed algorithms
and one of them can detect malicious agents with only the information from
direct in-neighbors. For the averaging part, we extend the applicability of an
existing averaging algorithm where normal agents can remove the effects from
malicious agents so far, after they are detected. Another important feature of
our method is that it can handle the case where malicious agents are
neighboring and collaborating with each other to mislead the normal ones from
averaging. This case cannot be solved by existing detection approaches in
related literature. Moreover, our algorithm is efficient in storage usage
especially for large-scale networks as each agent only requires the values of
neighbors within two hops. Lastly, numerical examples are given to verify the
efficacy of the proposed algorithms.
