  In this paper, we consider the problem of distributed online convex
optimization, where a network of local agents aim to jointly optimize a convex
function over a period of multiple time steps. The agents do not have any
information about the future. Existing algorithms have established dynamic
regret bounds that have explicit dependence on the number of time steps. In
this work, we show that we can remove this dependence assuming that the local
objective functions are strongly convex. More precisely, we propose a gradient
tracking algorithm where agents jointly communicate and descend based on
corrected gradient steps. We verify our theoretical results through numerical
experiments.
