Publications related to Windows Server 2016

Robust Collaborative Learning with Linear Gradient Overhead

Rachid Guerraoui, Nirupam Gupta, John Stephan, Sadegh Farhadkhani, Le Nguyen Hoang, Rafaël Benjamin Pinot

Collaborative learning algorithms, such as distributed SGD (or D-SGD), are prone to faulty machines that may deviate from their prescribed algorithm because of software or hardware bugs, poisoned data or malicious behaviors. While many solutions have been ...

PLMR2023

Genuinely distributed Byzantine machine learning

Rachid Guerraoui, El Mahdi El Mhamdi, Le Nguyen Hoang, Sébastien Louis Alexandre Rouault, Arsany Hany Abdelmessih Guirguis

Machine learning (ML) solutions are nowadays distributed, according to the so-called server/worker architecture. One server holds the model parameters while several workers train the model. Clearly, such architecture is prone to various types of component ...

2022

Practical Byzantine-resilient Stochastic Gradient Descent

Sébastien Louis Alexandre Rouault

Algorithms are everywhere.The recipe for the frangipane cake is an algorithm.If all the listed ingredients are available and the cook is sufficiently deft, after a finite number of small, well-defined steps a delicious dessert will exit the oven.Now, what ...

EPFL2022

LENA: Communication-Efficient Distributed Learning with Self-Triggered Gradient Uploads

Martin Jaggi, Sebastian Urban Stich, Hossein Shokri Ghadikolaei

In distributed optimization, parameter updates from the gradient computing node devices have to be aggregated in every iteration on the orchestrating server. When these updates are sent over an arbitrary commodity network, bandwidth and latency can be limi ...

MICROTOME PUBLISHING2021

Resource Management for Power-Constrained HEVC Transcoding Using Reinforcement Learning

David Atienza Alonso, Marina Zapater Sancho, Luis Maria Costero Valero, Arman Iranfar

The advent of online video streaming services along with the users' demand for high-quality contents require High Efficiency Video Coding (HEVC), which provides higher quality and compression at the cost of increased complexity. On one hand, HEVC exposes a ...

2020

A system design for elastically scaling transaction processing engines in virtualized servers

Anastasia Ailamaki, Angelos Christos Anadiotis, Raja Appuswamy, Hillel Avni

Online Transaction Processing (OLTP) deployments are migrating from on-premise to cloud settings in order to exploit the elasticity of cloud infrastructure which allows them to adapt to workload variations. However, cloud adaptation comes at the cost of re ...

ASSOC COMPUTING MACHINERY2020

Genuinely Distributed Byzantine Machine Learning

Rachid Guerraoui, El Mahdi El Mhamdi, Le Nguyen Hoang, Sébastien Louis Alexandre Rouault, Arsany Hany Abdelmessih Guirguis

Machine Learning (ML) solutions are nowadays distributed, according to the so-called server/worker architecture. One server holds the model parameters while several workers train the model. Clearly, such architecture is prone to various types of component ...

Association for Computing Machinery2020

Gem5-X: A Gem5-Based System Level Simulation Framework to Optimize Many-Core Platforms

David Atienza Alonso, Marina Zapater Sancho, William Andrew Simon, Yasir Mahmood Qureshi

The rapid expansion of online-based services requires novel energy and performance efficient architectures to meet power and latency constraints. Fast architectural exploration has become a key enabler in the proposal of architectural innovation. In this p ...

IEEE2019

Functionality Enhanced Memories for Edge-AI Embedded Systems

David Atienza Alonso, Alexandre Sébastien Julien Levisse, Pierre-Emmanuel Julien Marc Gaillardon, Marco Antonio Rios, William Andrew Simon

With the surge in complexity of edge workloads, it appeared in the scientific community that such workloads cannot be anymore overflown to the cloud due to the huge edge device to server communication energy cost and the high energy consumption induced in ...

IEEE2019

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Windows Server 2016

Robust Collaborative Learning with Linear Gradient Overhead

Genuinely distributed Byzantine machine learning

Practical Byzantine-resilient Stochastic Gradient Descent

LENA: Communication-Efficient Distributed Learning with Self-Triggered Gradient Uploads

Resource Management for Power-Constrained HEVC Transcoding Using Reinforcement Learning

A system design for elastically scaling transaction processing engines in virtualized servers

Genuinely Distributed Byzantine Machine Learning

Timed-Release Encryption With Master Time Bound Key

Gem5-X: A Gem5-Based System Level Simulation Framework to Optimize Many-Core Platforms

Functionality Enhanced Memories for Edge-AI Embedded Systems

Graph Chatbot

Chat with Graph Search

Robust Collaborative Learning with Linear Gradient Overhead

LENA: Communication-Efficient Distributed Learning with Self-Triggered Gradient Uploads

Resource Management for Power-Constrained HEVC Transcoding Using Reinforcement Learning

Gem5-X: A Gem5-Based System Level Simulation Framework to Optimize Many-Core Platforms

Practical Byzantine-resilient Stochastic Gradient Descent

Genuinely distributed Byzantine machine learning

A system design for elastically scaling transaction processing engines in virtualized servers

Timed-Release Encryption With Master Time Bound Key

Functionality Enhanced Memories for Edge-AI Embedded Systems

Genuinely Distributed Byzantine Machine Learning