15 citations · 45 across the 17 of their papers we have counts for
6 papers · 1 filter
Efficient Pyramidal Analysis of Gigapixel Images on a Decentralized Modest Computer Cluster
Marie Reinbigler, Rishi Sharma, Rafael Pires +3
Analyzing gigapixel images is recognized as computationally demanding. In this paper, we introduce PyramidAI, a technique for analyzing gigapixel images with reduced computational…
HarMoEny: Efficient Multi-GPU Inference of MoE Models
Zachary Doucet, Rishi Sharma, Martijn de Vos +3
Mixture-of-Experts (MoE) models offer computational efficiency during inference by activating only a subset of specialized experts for a given input. This enables efficient model s…
Boosting Asynchronous Decentralized Learning with Model Fragmentation
Sayan Biswas, Anne-Marie Kermarrec, Alexis Marouani +3
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…
Noiseless Privacy-Preserving Decentralized Learning
Sayan Biswas, Mathieu Even, Anne-Marie Kermarrec +4
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…
Get More for Less in Decentralized Learning Systems
Akash Dhasade, Anne-Marie Kermarrec, Rafael Pires +3
Decentralized learning (DL) systems have been gaining popularity because they avoid raw data sharing by communicating only model parameters, hence preserving data confidentiality.…
Decentralized Learning Made Easy with DecentralizePy
Akash Dhasade, Anne-Marie Kermarrec, Rafael Pires +2
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 withou…