论文标题
通过网络的异步贝叶斯学习
Asynchronous Bayesian Learning over a Network
论文作者
论文摘要
我们为网络代理提供了实用的异步数据融合模型，以执行分布式贝叶斯学习而无需共享原始数据。我们的算法使用基于八卦的方法，其中成对随机选择的代理使用未经调整的Langevin动力学进行参数采样。我们还引入了一种事件触发的机制，以进一步减少闲话剂之间的通信。这些机制大大减少了开销的沟通，并有助于避免常见的分布式算法经历的瓶颈。此外，算法减少的链路利用率有望提高偶尔连接故障的弹性。我们为算法建立数学保证，并通过数值实验证明其有效性。
We present a practical asynchronous data fusion model for networked agents to perform distributed Bayesian learning without sharing raw data. Our algorithm uses a gossip-based approach where pairs of randomly selected agents employ unadjusted Langevin dynamics for parameter sampling. We also introduce an event-triggered mechanism to further reduce communication between gossiping agents. These mechanisms drastically reduce communication overhead and help avoid bottlenecks commonly experienced with distributed algorithms. In addition, the reduced link utilization by the algorithm is expected to increase resiliency to occasional link failure. We establish mathematical guarantees for our algorithm and demonstrate its effectiveness via numerical experiments.
