Optimistic Causal Consistency for Geo-Replicated Key-Value Stores

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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 ...

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 ...

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 ...

Uncertainty quantification is a central challenge in reliable and trustworthy machine learning. Naive measures such as last-layer scores are well-known to yield overconfident estimates in the context of overparametrized neural networks. Several methods, ra ...

In this paper, we investigate federated contextual linear bandit learning within a wireless system that comprises a server and multiple devices. Each device interacts with the environment, selects an action based on the received reward, and sends model upd ...

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 ...

Personalized ranking methods are at the core of many systems that learn to produce recommendations from user feedbacks. Their primary objective is to identify relevant items from very large vocabularies and to assist users in discovering new content. These ...

Online services have become ubiquitous in technological society, the global demand for which has driven enterprises to construct gigantic datacenters that run their software. Such facilities have also recently become a substrate for third-party organizatio ...

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 ...

We present GARFIELD, a library to transparently make machine learning (ML) applications, initially built with popular (but fragile) frameworks, e.g., TensorFlow and PyTorch, Byzantine–resilient. GARFIELD relies on a novel object–oriented design, reducing t ...

We exploit differences across U.S. states' exposure to trade to study the effects of changes in the exchange rate on economic activity. Across states, trade-weighted exchange rate depreciations are associated with increased state exports, reduced state une ...

The single-server multi-message private information retrieval with side information problem is studied for general cases. In this problem, K independent messages are stored at a single server. One user initially has M messages as side information and wants ...

Modern large-scale data platforms manage colossal amount of data, generated by the ever-increasing number of concurrent users. Geo-replicated and sharded key-value data stores play a central role when building such platforms. As the strongest consistency m ...

Federated Learning (FL) is a machine learning setting where many devices collaboratively train a machine learning model while keeping the training data decentralized. In most of the current training schemes the central model is refined by averaging the par ...

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 ...

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 ...

We consider the machine teaching problem in a classroom-like setting wherein the teacher has to deliver the same examples to a diverse group of students. Their diversity stems from differences in their initial internal states as well as their learning rate ...

Little is known about the life-history trade-offs and limitations, and the physiological mechanisms that are associated with phenotypic adaptation to future ocean conditions. To address this knowledge gap, we investigated the within-and trans-generation li ...

A tolerancing method highlighting trade-offs against key design variables of mechanical systems is proposed and applied to herringbone-grooved gas journal bearings. Gas bearings typically suffer from a subsynchronous instability, demanding a very tight tol ...

To meet today's data management needs, it is a widespread practice to use distributed data storage and processing systems. Since the publication of the MapReduce paradigm, a plethora of such systems arose, but although widespread, the capabilities of these ...

Optimistic Causal Consistency for Geo-Replicated Key-Value Stores

Chattez avec Graph Search

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

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

Privatized graph federated learning

On double-descent in uncertainty quantification in overparametrized models

Federated Linear Bandit Learning via Over-the-air Computation

Oracular Byzantine Reliable Broadcast

Dynamic Personalized Ranking

Hardware and Software Support for RPC-Centric Server Architecture

When to Hedge in Interactive Services

GARFIELD: System Support for Byzantine Machine Learning (Regular Paper)

Regional Effects of Exchange Rate Fluctuations

Single-server Multi-message Private Information Retrieval with Side Information: the General Cases

Efficient Protocols for Enforcing Causal Consistency in Geo-Replicated Key-Value Data Stores

Ensemble Distillation for Robust Model Fusion in Federated Learning

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

Drowsy-DC: Data center power management system

Iterative Classroom Teaching

Life-history trade-offs and limitations associated with phenotypic adaptation under future ocean warming and elevated salinity

Multi-Objective Optimization of Grooved Gas Journal Bearings for Robustness in Manufacturing Tolerances

Formal Approaches to Querying Big Data in Shared-Nothing Systems