Social Learning under Randomized Collaborations

Autor:	Inan, Yunus, Kayaalp, Mert, Telatar, Emre, Sayed, Ali H.
Rok vydání:	2022
Předmět:	Computer Science - Multiagent Systems Computer Science - Social and Information Networks Electrical Engineering and Systems Science - Signal Processing
Druh dokumentu:	Working Paper
Popis:	We study a social learning scheme where at every time instant, each agent chooses to receive information from one of its neighbors at random. We show that under this sparser communication scheme, the agents learn the truth eventually and the asymptotic convergence rate remains the same as the standard algorithms which use more communication resources. We also derive large deviation estimates of the log-belief ratios for a special case where each agent replaces its belief with that of the chosen neighbor. Comment: Accepted for ISIT 2022
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2201.10957 Zobrazit plný text záznamu View this record from Arxiv