7 accepted papers
Communication-efficient approaches for decentralized learning rely on the exchange of quantized signals coupled with local updates. In this context, differential quantization is an effective technique to mitigate the negative impact of quantization by leveraging correlations between subsequent itera…
In this work, we present and study a low-precision variant of the stochastic gradient descent (SGD) algorithm with adaptive quantization. In particular, fixed-rate probabilistic uniform quantizers with varying quantization steps and mid-values are used to compress the parameter vectors. Gradient cli…
Observations collected by agents in a network may be unreliable due to observation noise or interference. This paper proposes a distributed algorithm that allows each node to improve the reliability of its own observation by relying solely on local computations and interactions with immediate neighb…
This paper considers optimization problems over networks where agents have individual objectives to meet, or individual parameter vectors to estimate, subject to subspace constraints that enforce the objectives across the network to lie in a low-dimensional subspace. This constrained formulation inc…
Most works on graph signal processing assume static graph signals, which is a limitation even in comparison to traditional DSP techniques where signals are modeled as sequences that evolve over time. For broader applicability, it is necessary to develop techniques that are able to process dynamic or…
Diffusion LMS is an efficient strategy for solving distributed optimization problems with cooperating agents. In some applications, the optimum parameter vectors may not be the same for all agents. Moreover, agents usually exchange information through noisy communication links. In this work, we anal…
In this work, a diffusion-type algorithm is proposed to solve multitask estimation problems where each cluster of nodes is interested in estimating its own optimum parameter vector in a distributed manner. The approach relies on minimizing a global mean-square error criterion regularized by a term t…