This work considers the problem of decentralized online learning, where the
goal is to track the optimum of the sum of time-varying functions, distributed
across several nodes in a network. The local availability of the functions and
their gradients necessitates coordination and consensus among the nodes. We put
forth the Generalized Gradient Tracking (GGT) framework that unifies a number
of existing approaches, including the state-of-the-art ones. The performance of
the proposed GGT algorithm is theoretically analyzed using a novel semidefinite
programming-based analysis that yields the desired regret bounds under very
general conditions and without requiring the gradient boundedness assumption.
The results are applicable to the special cases of GGT, which include various
state-of-the-art algorithms as well as new dynamic versions of various
classical decentralized algorithms. To further minimize the regret, we consider
a condensed version of GGT with only four free parameters. A procedure for
offline tuning of these parameters using only the problem parameters is also
detailed. The resulting optimized GGT (oGGT) algorithm not only achieves
improved dynamic regret bounds, but also outperforms all state-of-the-art
algorithms on both synthetic and real-world datasets.

本文提出了一种基于广义梯度跟踪（GGT）框架的去中心化在线学习算法，并使用新的半定编程分析理论对其性能进行了理论分析和优化，进而得到实际数据集上的优异表现。