Publications by Rhicheek Patra

FLEET: Online Federated Learning via Staleness Awareness and Performance Prediction

Rachid Guerraoui, Anne-Marie Kermarrec, Rhicheek Patra

Federated learning (FL) is very appealing for its privacy benefits: essentially, a global model is trained with updates computed on mobile devices while keeping the data of users local. Standard FL infrastructures are however designed to have no energy or ...

ASSOC COMPUTING MACHINERY2022

FLeet: Online Federated Learning via Staleness Awareness and Performance Prediction

Rachid Guerraoui, Anne-Marie Kermarrec, Rhicheek Patra, Georgios Damaskinos, Vlad-Tiberiu Nitu

Federated Learning (FL) is very appealing for its privacy benefits: essentially, a global model is trained with updates computed on mobile devices while keeping the data of users local. Standard FL infrastructures are however designed to have no energy or ...

Association for Computing Machinery2020

Unified and Scalable Incremental Recommenders with Consumed Item Packs

Rachid Guerraoui, Rhicheek Patra

Recommenders personalize the web content using collaborative filtering to relate users (or items). This work proposes to unify user-based, item-based and neural word embeddings types of recommenders under a single abstraction for their input, we name Consu ...

Springer2019

Asynchronous Byzantine Machine Learning (the case of SGD)

Rachid Guerraoui, Mahsa Taziki, El Mahdi El Mhamdi, Rhicheek Patra, Georgios Damaskinos

Asynchronous distributed machine learning solutions have proven very effective so far, but always assuming perfectly functioning workers. In practice, some of the workers can however exhibit Byzantine behavior, caused by hardware failures, software bugs, c ...

2018

Towards Scalable Personalization

Rhicheek Patra

The ever-growing amount of online information calls for Personalization. Among the various personalization systems, recommenders have become increasingly popular in recent years. Recommenders typically use collaborative filtering to suggest the most releva ...

EPFL2018

Capturing the Moment: Lightweight Similarity Computations

Rachid Guerraoui, Rhicheek Patra, Georgios Damaskinos

Similarity computations are crucial in various web activities like advertisements, search or trust-distrust predictions. These similarities often vary with time as product perception and popularity constantly change with users' evolving inclination. The hu ...

IEEE2017

Heterogeneous Recommendations: What You Might Like To Read After Watching Interstellar

Rachid Guerraoui, Anne-Marie Kermarrec, Rhicheek Patra, Tao Lin

Recommenders, as widely implemented nowadays by major e-commerce players like Netflix or Amazon, use collaborative filtering to suggest the most relevant items to their users. Clearly, the effectiveness of recommenders depends on the data they can exploit, ...

Assoc Computing Machinery2017

Frugal Topology Construction for Stream Aggregation in the Cloud

Rachid Guerraoui, Rhicheek Patra, Bao Duy Tran

Aggregation of streamed data is key to the expansion of the Internet of Things. This paper addresses the problem of designing a topology for reliably aggregating data flows from many devices arriving at a datacenter. Reliability here means ensuring operati ...

Ieee2016

D2P: Distance-Based Differential Privacy in Recommenders

Rachid Guerraoui, Anne-Marie Kermarrec, Mahsa Taziki, Rhicheek Patra

The upsurge in the number of web users over the last two decades has resulted in a significant growth of online information. This information growth calls for recommenders that personalize the in- formation proposed to each individual user. Nevertheless, p ...

Assoc Computing Machinery2015