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Analysis of Spatial and Incremental LMS Processing for Distributed Estimation

Publications associées (65)

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Adaptation and Learning Over Networks Under Subspace Constraints-Part I: Stability Analysis

Ali H. Sayed, Stefan Vlaski, Roula Nassif

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 require the objectives across the network to lie in low-dimensional ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Distributed Learning over Networks under Subspace Constraints

Ali H. Sayed, Stefan Vlaski, Roula Nassif

This work presents and studies a distributed algorithm for solving optimization problems over networks where agents have individual costs to minimize subject to subspace constraints that require the minimizers across the network to lie in a low-dimensional ...

IEEE2019

Optimized Quantization in Distributed Graph Signal Processing

Pascal Frossard, Isabela Cunha Maia Nobre

Distributed graph signal processing methods require that the graph nodes communicate by exchanging messages. These messages have a finite precision in a realistic network, which may necessitate to implement quantization. Quantization, in turn, generates er ...

IEEE2019

Distributed Inference Over Networks Under Subspace Constraints

Ali H. Sayed, Stefan Vlaski, Roula Nassif

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-dimensiona ...

IEEE2019

5G cellular networks will enable new differentiated services and applications to support industry like healthcare, energy, automobile and security. These services will massively increase the number of attached devices like mobiles, sensors, and actuators, ...

Ieee2018

Scalable Bias-Resistant Distributed Randomness

Bryan Alexander Ford, Linus Gasser, Eleftherios Kokoris Kogias, Philipp Svetolik Jovanovic, Nicolas Gailly, Ismail Khoffi

Bias-resistant public randomness is a critical component in many (distributed) protocols. Existing solutions do not scale to hundreds or thousands of participants, as is needed in many decentralized systems. We propose two large-scale distributed protocols ...

Ieee2017

Networks are central in the modeling and analysis of many large-scale human and technical systems, and they have applications in diverse fields such as computer science, biology, social sciences, and economics. Recently, network mining has been an active a ...

EPFL2016

In this work and the supporting Parts II and III of this paper, also in the current issue, we provide a rather detailed analysis of the stability and performance of asynchronous strategies for solving distributed optimization and adaptation problems over n ...

Institute of Electrical and Electronics Engineers2015

Recent advances in data processing and communication systems have led to a continuous increase in the amount of data communicated over today’s networks. These large volumes of data pose new challenges on the current networking infrastructure that only offe ...

EPFL2013

This work shows how the combination weights of diffusion strategies for adaptation and learning over networks can be chosen in order for the network mean-square-error performance to match that of an optimized centralized (or batch) solution. The results sh ...

IEEE2013