This paper develops algorithms to train support vector machines when training data are distributed across different nodes, and their communication to a centralized processing unit...
Pedro A. Forero, Alfonso Cano, Georgios B. Giannak...
Abstract--In this paper, we propose a decentralized sensor network scheme capable to reach a globally optimum maximum-likelihood (ML) estimate through self-synchronization of nonli...
—Ensuring spontaneous ad hoc interoperation in decentralized ubiquitous computing environments is challenging, because of heterogeneous resources and divergent policies. Centrali...
Venkatraman Ramakrishna, Peter L. Reiher, Leonard ...
Distributed averaging describes a class of network algorithms for the decentralized computation of aggregate statistics. Initially, each node has a scalar data value, and the goal...
Many applications in multiagent learning are essentially convex optimization problems in which agents have only limited communication and partial information about the function be...
Renato L. G. Cavalcante, Alex Rogers, Nicholas R. ...