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This work develops an exact converging algorithm for the solution of a distributed optimization problem with partially-coupled parameters across agents in a multi-agent scenario. In this formulation, while the network performance is dependent on a collecti ...
Part I of this work developed the exact diffusion algorithm to remove the bias that is characteristic of distributed solutions for deterministic optimization problems. The algorithm was shown to be applicable to a larger set of combination policies than ea ...
In empirical risk optimization, it has been observed that gradient descent implementations that rely on random reshuffling of the data achieve better performance than implementations that rely on sampling the data randomly and independently of each other. ...
Diffusion adaptive networks tasked with solving estimation problems have attracted attention in recent years due to their reliability, scalability, resource efficiency, and resilience to node and link failure. Diffusion adaptation strategies that are based ...
Online learning with streaming data in a distributed and collaborative manner can be useful in a wide range of applications. This topic has been receiving considerable attention in recent years with emphasis on both single-task and multitask scenarios. In ...
In diffusion social learning over weakly-connected graphs, it has been shown that influential agents end up shaping the beliefs of non-influential agents. In this paper, we analyse this control mechanism more closely and reveal some critical properties. In ...
We consider distributed multitask learning problems over a network of agents where each agent is interested in estimating its own parameter vector, also called task, and where the tasks at neighboring agents are related according to a set of linear equalit ...
In many scenarios of interest, agents may only have access to partial information about an unknown model or target vector. Each agent may be sensing only a subset of the entries of a global target vector, and the number of these entries can be different ac ...
This work examines the mean-square error performance of diffusion stochastic algorithms under a generalized coordinate-descent scheme. In this setting, the adaptation step by each agent is limited to a random subset of the coordinates of its stochastic gra ...
We consider the problem of decentralized clustering and estimation over multitask networks, where agents infer and track different models of interest. The agents do not know beforehand which model is generating their own data. They also do not know which a ...
