Publications de Rafael Pereira Pires

Efficient Pyramidal Analysis of Gigapixel Images on a Decentralized Modest Computer Cluster

Anne-Marie Kermarrec, Rafael Pereira Pires, Rishi Sharma

Analyzing gigapixel images is recognized as computationally demanding. In this paper, we introduce PyramidAI, a technique for analyzing gigapixel images with reduced computational cost. The proposed approach adopts a gradual analysis of the image, beginnin ...

Springer Nature Switzerland2025

Boosting Resource-Constrained Federated Learning Systems with Guessed Updates

Anne-Marie Kermarrec, Akash Balasaheb Dhasade, Rafael Pereira Pires, Rishi Sharma

Federated learning (FL) enables a set of client devices to collaboratively train a model without sharing raw data. This process, though, operates under the constrained computation and communication resources of edge devices. These constraints combined with ...

Institute of Electrical and Electronics Engineers (IEEE)2025

Harnessing Increased Client Participation with Cohort-Parallel Federated Learning

Anne-Marie Kermarrec, Akash Balasaheb Dhasade, Rafael Pereira Pires, Marinus Abraham de Vos

Federated learning (FL) is a machine learning approach where nodes collaboratively train a global model. As more nodes participate in a round of FL, the effectiveness of individual model updates by nodes also diminishes. In this study, we increase the effe ...

ACM2025

Boosting Asynchronous Decentralized Learning with Model Fragmentation

Anne-Marie Kermarrec, Rafael Pereira Pires, Rishi Sharma, Marinus Abraham de Vos, Sayan Biswas

Decentralized learning (DL) is an emerging technique that allows nodes on the web to collaboratively train machine learning models without sharing raw data. Dealing with stragglers, i.e., nodes with slower compute or communication than others, is a key cha ...

ACM2025

Low-Cost Privacy-Preserving Decentralized Learning

Anne-Marie Kermarrec, Rafael Pereira Pires, Rishi Sharma, Sayan Biswas

Decentralized learning (DL) is an emerging paradigm of collaborative machine learning that enables nodes in a network to train models collectively without sharing their raw data or relying on a central server. This paper introduces Zip-DL, a privacy-aware ...

Privacy Enhancing Technologies Symposium Advisory Board2025

Efficient Federated Search for Retrieval-Augmented Generation

Rachid Guerraoui, Anne-Marie Kermarrec, Diana Andreea Petrescu, Rafael Pereira Pires, Marinus Abraham de Vos

Large language models (LLMs) have demonstrated remarkable capabilities across various domains but remain susceptible to hallucinations and inconsistencies, limiting their reliability. Retrieval-augmented generation (RAG) mitigates these issues by grounding ...

ACM2025

Accelerating MoE Model Inference with Expert Sharding

Anne-Marie Kermarrec, Milos Vujasinovic, Rafael Pereira Pires, Marinus Abraham de Vos

Mixture of experts (MoE) models achieve state-of-the-art results in language modeling but suffer from inefficient hardware utilization due to imbalanced token routing and communication overhead. While prior work has focused on optimizing MoE training and d ...

Association for Computing Machinery2025

Energy-Aware Decentralized Learning with Intermittent Model Training

Anne-Marie Kermarrec, Akash Balasaheb Dhasade, Rafael Pereira Pires, Rishi Sharma, Marinus Abraham de Vos

SKIPTRAIN is a novel Decentralized Learning (DL) algorithm, which minimizes energy consumption in decentralized learning by strategically skipping some training rounds and substituting them with synchronization rounds. These trainingsilent periods, besides ...

IEEE2024

Revisiting Ensembling in One-Shot Federated Learning

Rachid Guerraoui, Anne-Marie Kermarrec, Akash Balasaheb Dhasade, Rafael Pereira Pires, Nirupam Gupta, Rafaël Benjamin Pinot, Rishi Sharma, Youssef Allouah

Federated learning (FL) is an appealing approach to training machine learning models without sharing raw data. However, standard FL algorithms are iterative and thus induce a significant communication cost. One-shot federated learning (OFL) trades the iter ...

Curran Associates, Inc.2024

Noiseless Privacy-Preserving Decentralized Learning

Anne-Marie Kermarrec, Rafael Pereira Pires, Rishi Sharma, Marinus Abraham de Vos, Sayan Biswas

Decentralized learning (DL) enables collaborative learning without a server and without training data leaving the users' devices. However, the models shared in DL can still be used to infer training data. Conventional defenses such as differential privacy ...

Privacy Enhancing Technologies Symposium Advisory Board2024

Decentralized learning made easy with DecentralizePy

Anne-Marie Kermarrec, Akash Balasaheb Dhasade, Milos Vujasinovic, Rafael Pereira Pires, Rishi Sharma

Decentralized learning (DL) has gained prominence for its potential benefits in terms of scalability, privacy, and fault tolerance. It consists of many nodes that coordinate without a central server and exchange millions of parameters in the inherently ite ...

2023

Epidemic Learning: Boosting Decentralized Learning with Randomized Communication

Rachid Guerraoui, Anne-Marie Kermarrec, Sadegh Farhadkhani, Rafael Pereira Pires, Rishi Sharma, Marinus Abraham de Vos

We present Epidemic Learning ( EL ), a simple yet powerful decentralized learning (DL) algorithm that leverages changing communication topologies to achieve faster model convergence compared to conventional DL approaches. At each round of EL, each node sen ...

2023

Get More for Less in Decentralized Learning Systems

Anne-Marie Kermarrec, Jeffrey Wigger, Akash Balasaheb Dhasade, Milos Vujasinovic, Rafael Pereira Pires, Rishi Sharma

Decentralized learning (DL) systems have been gaining popularity because they avoid raw data sharing by communicating only model parameters, hence preserving data confidentiality. However, the large size of deep neural networks poses a significant challeng ...

IEEE2023

TEE-based decentralized recommender systems: The raw data sharing redemption

Anne-Marie Kermarrec, Akash Balasaheb Dhasade, Nevena Dresevic, Rafael Pereira Pires

Recommenders are central in many applications today. The most effective recommendation schemes, such as those based on collaborative filtering (CF), exploit similarities between user profiles to make recommendations, but potentially expose private data. Fe ...

2022

Malware in the SGX supply chain: Be careful when signing enclaves!

Pascal Felber, Rafael Pereira Pires

Malware attacks are a significant part of the new software security threats detected each year. Intel Software Guard Extensions (SGX) are a set of hardware instructions introduced by Intel in their recent lines of processors that are intended to provide a ...

2022

NVCache: A Plug-and-Play NVMM-based I/O Booster for Legacy Systems

Pascal Felber, Rafael Pereira Pires

This paper introduces NVCache, an approach that uses a non-volatile main memory (NVMM) as a write cache to improve the write performance of legacy applications. We compare NVCache against file systems tailored for NVMM (Ext4-DAX and NOVA) and with I/O-heav ...

IEEE2021