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Publications associées (55)

Graph Chatbot

Chattez avec Graph Search

Posez n’importe quelle question sur les cours, conférences, exercices, recherches, actualités, etc. de l’EPFL ou essayez les exemples de questions ci-dessous.

AVERTISSEMENT : Le chatbot Graph n'est pas programmé pour fournir des réponses explicites ou catégoriques à vos questions. Il transforme plutôt vos questions en demandes API qui sont distribuées aux différents services informatiques officiellement administrés par l'EPFL. Son but est uniquement de collecter et de recommander des références pertinentes à des contenus que vous pouvez explorer pour vous aider à répondre à vos questions.

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Federated learning with uncertainty-based client clustering for fleet-wide fault diagnosis

Olga Fink, Hao Lu, Chao Hu

Operators from various industries have been pushing the adoption of wireless sensing nodes for industrial monitoring, and such efforts have produced sizeable condition monitoring datasets that can be used to build diagnosis algorithms capable of warning ma ...

2024

PRO-Face C: Privacy-Preserving Recognition of Obfuscated Face via Feature Compensation

Touradj Ebrahimi, Lin Yuan, Xiao Pu, Yao Zhang, Hongbo Li

The advancement of face recognition technology has delivered substantial societal advantages. However, it has also raised global privacy concerns due to the ubiquitous collection and potential misuse of individuals' facial data. This presents a notable par ...

Ieee-Inst Electrical Electronics Engineers Inc2024

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 ...

USENIX2021

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Federated Averaging (FEDAVG) has emerged as the algorithm of choice for federated learning due to its simplicity and low communication cost. However, in spite of recent research efforts, its performance is not fully understood. We obtain tight convergence ...

JMLR-JOURNAL MACHINE LEARNING RESEARCH2020

ClaimChain: Improving the Security and Privacy of In-band Key Distribution for Messaging

Carmela González Troncoso, Bogdan Kulynych, Wouter Lueks

The social demand for email end-to-end encryption is barely supported by mainstream service providers. Autocrypt is a new community -driven open specification for e-mail encryption that attempts to respond to this demand. In Autocrypt the encryption keys a ...

ACM2018

Flow control for Latency-Critical RPCs

Edouard Bugnion, Evangelos Marios Kogias

In today’s modern datacenters, the waiting time spent within a server’s queue is a major contributor of the end-to-end tail latency of μs-scale remote procedure calls. In traditional TCP, congestion control handles in-network congestion, while flow control ...

2018

Profiling, Modelling and Facilitating Online Activism

Julia Proskurnia

The extensive and successful use of social media has enhanced and empowered a variety of movements all over the world in a way that is hard to achieve through conventional means. This has led to numerous studies that leverage online social data to describe ...

EPFL2017

Enabling the Automatic Generation of User Interfaces for Remote Laboratories

Denis Gillet, Christophe Salzmann, Wissam Halimi, Hagop Jamkojian

Remote laboratories are an important component of blended and dis- tance science and engineering education. By definition, they provide access to a physical lab in a distant location. Many architectures enabling remote laboratory systems exist, the most co ...

Springer2017

Template Induction over Unstructured Email Corpora

Julia Proskurnia

Unsupervised template induction over email data is a central component in applications such as information extraction, document classification, and auto-reply. The benefits of automatically generating such templates are known for structured data, e.g. mach ...

Optimistic Causal Consistency for Geo-Replicated Key-Value Stores

Willy Zwaenepoel, Diego Didona, Kristina Spirovska

In this paper we present a new approach to implementing causal consistency in geo-replicated data stores, which we call Optimistic Causal Consistency (OCC). The optimism in our approach lies in that updates from a remote data center are immediately made vi ...

Ieee Computer Soc2017