Michael G. Rabbat

TL;DR: This paper investigates a general class of distributed algorithms for "in-network" data processing, eliminating the need to transmit raw data to a central point, and shows that for a broad class of estimation problems the distributed algorithms converge to within an /spl epsi/-ball around the globally optimal value.

TL;DR: An overview of recent gossip algorithms work, including convergence rate results, which are related to the number of transmitted messages and thus the amount of energy consumed in the network for gossiping, and the use of gossip algorithms for canonical signal processing tasks including distributed estimation, source localization, and compression.

TL;DR: This article describes a very different approach to the decentralized compression of networked data, considering a particularly salient aspect of this struggle that revolves around large-scale distributed sources of data and their storage, transmission, and retrieval.

TL;DR: The fastMRI dataset is introduced, a large-scale collection of both raw MR measurements and clinical MR images that can be used for training and evaluation of machine-learning approaches to MR image reconstruction.

TL;DR: The main conclusion is that as the number of sensors in the network grows, in-network processing will always use less energy than a centralized algorithm, while maintaining a desired level of accuracy.