  This paper considers the distributed bandit convex optimization problem with
time-varying inequality constraints over a network of agents, where the goal is
to minimize network regret and cumulative constraint violation. Existing
distributed online algorithms require that each agent broadcasts its decision
to its neighbors at each iteration. To better utilize the limited communication
resources, we propose a distributed event-triggered online primal--dual
algorithm with two-point bandit feedback. Under several classes of
appropriately chosen decreasing parameter sequences and non-increasing
event-triggered threshold sequences, we establish dynamic network regret and
network cumulative constraint violation bounds. These bounds are comparable to
the results achieved by distributed event-triggered online algorithms with
full-information feedback. Finally, a numerical example is provided to verify
the theoretical results.
