A privacy-preserving decentralized randomized block-coordinate subgradient algorithm over time-varying networks

under local strong convexity and under local convexity, in which represents the number of iterations. Meanwhile, we show that the privacy of data can be protected by the proposed algorithm. The results of experiments demonstrate the computational benefit of our algorithm on two real-world datasets. The theoretical results are also verified by different experiments.