Rafael Pereira Pires

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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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

Rafael Pereira Pires

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

Decentralized learning made easy with DecentralizePy

Efficient Federated Search for Retrieval-Augmented Generation

Harnessing Increased Client Participation with Cohort-Parallel Federated Learning

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

Boosting Asynchronous Decentralized Learning with Model Fragmentation

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

Low-Cost Privacy-Preserving Decentralized Learning

Get More for Less in Decentralized Learning Systems

Energy-Aware Decentralized Learning with Intermittent Model Training

Noiseless Privacy-Preserving Decentralized Learning

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

Epidemic Learning: Boosting Decentralized Learning with Randomized Communication

Revisiting Ensembling in One-Shot Federated Learning