Learning at the edges has become increasingly important as large quantities of data are continually generated locally. Among others, this paradigm requires algorithms that are \emph{simple} (so that they can be executed by local devices), \emph{robust} (again uncertainty as data are continually generated), and \emph{reliable} in a distributed manner under network issues, especially delays. In this study, we investigate the problem of online convex optimization (\oco) under adversarial delayed feedback. We propose two projection-free algorithms for centralized and distributed settings in which they are carefully designed to achieve a regret bound of $O(\sqrt{B})$ where $B$ is the sum of delay, which is optimal for the OCO problem in the delay setting while still being projection-free. We provide an extensive theoretical study and experimentally validate the performance of our algorithms by comparing them with existing ones on real-world problems.