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Publications associées (160)

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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

EAST discharge prediction without integrating simulation results (vol 62, 126060, 2022)

Alessandro Pau, Xiaojuan Liu, Chenguang Wan

In the published paper titled 'EAST discharge prediction without integrating simulation results' (2022 Nucl. Fusion 62 126060), figure 5 used the wrong plot, and is not matched with table 3. We used the plot discussed with referees, the case when we do not ...

Bristol2023

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This paper introduces protocols for authenticated private information retrieval. These schemes enable a client to fetch a record from a remote database server such that (a) the server does not learn which record the client reads, and (b) the client either ...

Berkeley2023

Neural ADMIXTURE for rapid genomic clustering

Albert Dominguez Mantes

Characterizing the genetic structure of large cohorts has become increasingly important as genetic studies extend to massive, increasingly diverse biobanks. Popular methods decompose individual genomes into fractional cluster assignments with each cluster ...

SPRINGERNATURE2023

Personalized Privacy-Preserving Distributed Learning on Heterogeneous Data

Michael Christoph Gastpar, Aditya Pradeep

One major challenge in distributed learning is to efficiently learn for each client when the data across clients is heterogeneous or non iid (not independent or identically distributed). This provides a significant challenge as the data of the other client ...

2023

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

Oracular Byzantine Reliable Broadcast

Rachid Guerraoui, Manuel José Ribeiro Vidigueira, Martina Camaioni, Matteo Monti

Byzantine Reliable Broadcast (BRB) is a fundamental distributed computing primitive, with applications ranging from notifications to asynchronous payment systems. Motivated by practical consideration, we study Client-Server Byzantine Reliable Broadcast (CS ...

Schloss Dagstuhl - Leibniz-Zentrum für Informatik2022

Groove: Flexible Metadata-Private Messaging

Ludovic Barman, David Lazar

Metadata-private messaging designs that scale to support millions of users are rigid: they limit users to a single device that is online all the time and transmits on short regular intervals, and require users to choose precisely when each of their buddies ...

Berkeley2022

Reinforcement Learning-Based Joint Reliability and Performance Optimization for Hybrid-Cache Computing Servers

David Atienza Alonso, Marina Zapater Sancho, Luis Maria Costero Valero, Darong Huang, Ali Pahlevan

Computing servers play a key role in the development and process of emerging compute-intensive applications in recent years. However, they need to operate efficiently from an energy perspective viewpoint, while maximizing the performance and lifetime of th ...

2022

When to Hedge in Interactive Services

Edouard Bugnion, 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 ...

USENIX2021