Publications associées à Client (informatique)

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

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

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

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 ...

Usenix Assoc2022

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

SCAFFOLD Stochastic Controlled Averaging for Federated Learning

Sai Praneeth Reddy Karimireddy, Sebastian Urban Stich, Ananda Theertha Suresh

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

Pythia: Remote Oracles for the Masses

Mathias Josef Payer, Yuqing Zhang

Remote Direct Memory Access (RDMA) is a technology that allows direct access from the network to a machine's main memory without involving its CPU. RDMA offers low-latency, high-bandwidth performance and low CPU utilization. While RDMA provides massive per ...

USENIX ASSOC2019

Adaptive Streaming in Interactive Multiview Video Systems

Pascal Frossard, Laura Toni, Xue Zhang, Yao Zhao

Multiview applications endow final users with the possibility to freely navigate within 3D scenes with minimum-delay. A real feeling of scene navigation is enabled by transmitting multiple high-quality camera views, which can be used to synthesize addition ...

2019

Causal Consistency and Latency Optimality: Friend or Foe? [Extended Version]

Rachid Guerraoui, Willy Zwaenepoel, Diego Didona, Junxiong Wang

Causal consistency is an attractive consistency model for geo-replicated data stores. It is provably the strongest model that tolerates network partitions. It avoids the long latencies associated with strong consistency, and, especially when using read-onl ...

2018

Causal Consistency and Latency Optimality: Friend or Foe?

Rachid Guerraoui, Willy Zwaenepoel, Diego Didona, Junxiong Wang

Causal consistency is an attractive consistency model for geo-replicated data stores. It is provably the strongest model that tolerates network partitions. It avoids the long latencies associated with strong consistency, and, especially when using read-onl ...

ASSOC COMPUTING MACHINERY2018

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