7 research outputs found
Randomized Polar Codes for Anytime Distributed Machine Learning
We present a novel distributed computing framework that is robust to slow
compute nodes, and is capable of both approximate and exact computation of
linear operations. The proposed mechanism integrates the concepts of randomized
sketching and polar codes in the context of coded computation. We propose a
sequential decoding algorithm designed to handle real valued data while
maintaining low computational complexity for recovery. Additionally, we provide
an anytime estimator that can generate provably accurate estimates even when
the set of available node outputs is not decodable. We demonstrate the
potential applications of this framework in various contexts, such as
large-scale matrix multiplication and black-box optimization. We present the
implementation of these methods on a serverless cloud computing system and
provide numerical results to demonstrate their scalability in practice,
including ImageNet scale computations
Iterative Sketching for Secure Coded Regression
In this work, we propose methods for speeding up linear regression
distributively, while ensuring security. We leverage randomized sketching
techniques, and improve straggler resilience in asynchronous systems.
Specifically, we apply a random orthonormal matrix and then subsample
\textit{blocks}, to simultaneously secure the information and reduce the
dimension of the regression problem. In our setup, the transformation
corresponds to an encoded encryption in an \textit{approximate gradient coding
scheme}, and the subsampling corresponds to the responses of the non-straggling
workers; in a centralized coded computing network. This results in a
distributive \textit{iterative sketching} approach for an -subspace
embedding, \textit{i.e.} a new sketch is considered at each iteration. We also
focus on the special case of the \textit{Subsampled Randomized Hadamard
Transform}, which we generalize to block sampling; and discuss how it can be
modified in order to secure the data.Comment: 28 pages, 7 figures. arXiv admin note: substantial text overlap with
arXiv:2201.0852
Systematic Approaches for Telemedicine and Data Coordination for COVID-19 in Baja California, Mexico
Conference proceedings info:
ICICT 2023: 2023 The 6th International Conference on Information and Computer Technologies
Raleigh, HI, United States, March 24-26, 2023
Pages 529-542We provide a model for systematic implementation of telemedicine within a large evaluation center for COVID-19 in the area of Baja California, Mexico. Our model is based on human-centric design factors and cross disciplinary collaborations for scalable data-driven enablement of smartphone, cellular, and video Teleconsul-tation technologies to link hospitals, clinics, and emergency medical services for point-of-care assessments of COVID testing, and for subsequent treatment and quar-antine decisions. A multidisciplinary team was rapidly created, in cooperation with different institutions, including: the Autonomous University of Baja California, the Ministry of Health, the Command, Communication and Computer Control Center
of the Ministry of the State of Baja California (C4), Colleges of Medicine, and the College of Psychologists. Our objective is to provide information to the public and to evaluate COVID-19 in real time and to track, regional, municipal, and state-wide data in real time that informs supply chains and resource allocation with the anticipation of a surge in COVID-19 cases. RESUMEN Proporcionamos un modelo para la implementación sistemática de la telemedicina dentro de un gran centro de evaluación de COVID-19 en el área de Baja California, México. Nuestro modelo se basa en factores de diseño centrados en el ser humano y colaboraciones interdisciplinarias para la habilitación escalable basada en datos de tecnologÃas de teleconsulta de teléfonos inteligentes, celulares y video para vincular hospitales, clÃnicas y servicios médicos de emergencia para evaluaciones de COVID en el punto de atención. pruebas, y para el tratamiento posterior y decisiones de cuarentena. Rápidamente se creó un equipo multidisciplinario, en cooperación con diferentes instituciones, entre ellas: la Universidad Autónoma de Baja California, la SecretarÃa de Salud, el Centro de Comando, Comunicaciones y Control Informático.
de la SecretarÃa del Estado de Baja California (C4), Facultades de Medicina y Colegio de Psicólogos. Nuestro objetivo es proporcionar información al público y evaluar COVID-19 en tiempo real y rastrear datos regionales, municipales y estatales en tiempo real que informan las cadenas de suministro y la asignación de recursos con la anticipación de un aumento de COVID-19. 19 casos.ICICT 2023: 2023 The 6th International Conference on Information and Computer Technologieshttps://doi.org/10.1007/978-981-99-3236-