  While the last few decades have witnessed a huge body of work devoted to
inference and learning in distributed and decentralized setups, much of this
work assumes a non-adversarial setting in which individual nodes---apart from
occasional statistical failures---operate as intended within the algorithmic
framework. In recent years, however, cybersecurity threats from malicious
non-state actors and rogue entities have forced practitioners and researchers
to rethink the robustness of distributed and decentralized algorithms against
adversarial attacks. As a result, we now have a plethora of algorithmic
approaches that guarantee robustness of distributed and/or decentralized
inference and learning under different adversarial threat models. Driven in
part by the world's growing appetite for data-driven decision making, however,
securing of distributed/decentralized frameworks for inference and learning
against adversarial threats remains a rapidly evolving research area. In this
article, we provide an overview of some of the most recent developments in this
area under the threat model of Byzantine attacks.
