  This paper focuses on the distributed online convex optimization problem with
time-varying inequality constraints over a network of agents, where each agent
collaborates with its neighboring agents to minimize the cumulative
network-wide loss over time. To reduce communication overhead between the
agents, we propose a distributed event-triggered online primal-dual algorithm
over a time-varying directed graph. With several classes of appropriately chose
decreasing parameter sequences and non-increasing event-triggered threshold
sequences, we establish dynamic network regret and network cumulative
constraint violation bounds. Finally, a numerical simulation example is
provided to verify the theoretical results.
