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Publications associées (29)

Privatized graph federated learning

Ali H. Sayed, Stefan Vlaski, Elsa Rizk

Federated learning is a semi-distributed algorithm, where a server communicates with multiple dispersed clients to learn a global model. The federated architecture is not robust and is sensitive to communication and computational overloads due to its one-m ...

SPRINGER2023

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

When to Hedge in Interactive Services

Edouard Bugnion, Aikaterini Argyraki, Mia Primorac

In online data-intensive (OLDI) services, each client request typically executes on multiple servers in parallel; as a result, “system hiccups”, although rare within a single server, can interfere with many client requests and cause violations of service-l ...

USENIX ASSOC2021

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

MAGNETIC: Multi-Agent Machine Learning-Based Approach for Energy Efficient Dynamic Consolidation in Data Centers

David Atienza Alonso, Marina Zapater Sancho, Ali Pahlevan, Kosar Haghshenas

Improving the energy efficiency of data centers while guaranteeing Quality of Service (QoS), together with detecting performance variability of servers caused by either hardware or software failures, are two of the major challenges for efficient resource m ...

2022

Mitigating Load Imbalance in Distributed Data Serving with Rack-Scale Memory Pooling

Babak Falsafi, Edouard Bugnion, Boris Robert Grot, Alexandros Daglis, Stanko Novakovic, Dmitrii Ustiugov

To provide low-latency and high-throughput guarantees, most large key-value stores keep the data in the memory of many servers. Despite the natural parallelism across lookups, the load imbalance, introduced by heavy skew in the popularity distribution of k ...

2019

Drowsy-DC: Data center power management system

Willy Zwaenepoel, Baptiste Joseph Eustache Lepers, Mathieu Paul Fernand Bacou

In a modern data center (DC), a large majority of costs arise from energy consumption. The most popular technique used to mitigate this issue is virtualization and more precisely virtual machine (VM) consolidation. Although consolidation may increase serve ...

IEEE2019

Successive Refinement to Caching for Dynamic Content

Michael Christoph Gastpar, Pinar Sen

To reduce the network load during peak hours, servers deliver partial data to users during the off-peak time of the network before the actual requests are known, which is known as caching. This paper studies a single user caching problem in which the file ...

IEEE2019

Systems and method for anonymous, low-latencey, tracking-resistant communications in a networked environment

Jean-Pierre Hubaux, Bryan Alexander Ford, Italo Ivan Dacosta Petrocelli, Ludovic Barman

Exemplary embodiments of the present disclosure provide cryptographically-strong tracking protection with unnoticeable "single-hop" network latencies by leveraging a client-relay- server architecture, in which the clients, relays (trusted or untrusted), an ...

2018

Load Balancing Video Clients in Enterprise Wireless Networks

Dan-Cristian Tomozei, Lorenzo Granai, Aswin Suresh, Sergio Mena

Given the increasing popularity of real-time video communication over enterprise wireless networks, ensuring good quality of experience is becoming critical. A common problem in such networks is that the clients typically adopt the strategy of associating ...

IEEE2018

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