Decentralized Stochastic Optimization With Random Attendance

We design a decentralized stochastic optimization algorithm for machine-to-machine (M2M) networks, in which machines in the networks can attend the optimization process in a randomly-unexpected manner. This property of random attendance aims at capturing unexpected events that prevent the machines to successfully communicate with each other. The property, beside simulating the unforeseen nature of the networks, additionally helps reduce the communication cost of machines while still maintaining a reasonable optimization outcome. Our contribution gives practical insights for deploying optimization algorithms over machines in complex environments, including the Internet of Things.