• Corpus ID: 59222801

Asynchronous Decentralized Optimization in Directed Networks

@article{Zhang2019AsynchronousDO,
  title={Asynchronous Decentralized Optimization in Directed Networks},
  author={Jiaqi Zhang and Keyou You},
  journal={ArXiv},
  year={2019},
  volume={abs/1901.08215}
}

• Jiaqi Zhang, Keyou You
• Published 24 January 2019
• Computer Science
• ArXiv

A popular asynchronous protocol for decentralized optimization is randomized gossip where a pair of neighbors concurrently update via pairwise averaging. In practice, this creates deadlocks and is vulnerable to information delays. It can also be problematic if a node is unable to response or has only access to its private-preserved local dataset. To address these issues simultaneously, this paper proposes an asynchronous decentralized algorithm, i.e. APPG, with {\em directed} communication where each node updates {\em asynchronously} and independently of any other node. If local functions are strongly-convex with Lipschitz-continuous gradients, each node of APPG converges to the same optimal solution at a rate of $O(\lambda^k)$, where $\lambda\in(0,1)$ and the virtual counter $k$ increases by 1 no matter on which node updates. The superior performance of APPG is validated on a logistic regression problem against state-of-the-art methods in terms of linear speedup and system implementations.