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 simple (so that they can be executed by local devices),
robust (again uncertainty as data are continually generated), and reliable in a
distributed manner under network issues, especially delays. In this study, we
investigate the problem of online convex optimization under adversarial delayed
feedback. We propose two projection-free algorithms for centralised 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.

本研究中，我们在边缘学习方面进行了调查，探讨了在线凸优化问题下的对抗性延迟反馈，提出了两种无投影算法，用于集中式和分布式环境中，通过与现有方法在真实世界问题上的比较，我们理论上和实验证明了算法的性能，实现了延迟环境中 OCO 问题的 O (√B) 的后悔界。