Publications de Rishi Sharma | EPFL Graph Search

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, Rafael Pereira Pires, Akash Balasaheb Dhasade, 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

Fair Decentralized Learning

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

Decentralized learning (DL) is an emerging approach that enables nodes to collaboratively train a machine learning model without sharing raw data. In many application domains, such as healthcare, this approach faces challenges due to the high level of hete ...

IEEE2025

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

Practical Federated Learning without a Server

Anne-Marie Kermarrec, Akash Balasaheb Dhasade, Rishi Sharma, Marinus Abraham de Vos, Erick Lavoie

Federated Learning (FL) enables end-user devices to collaboratively train ML models without sharing raw data, thereby preserving data privacy. In FL, a central parameter server coordinates the learning process by iteratively aggregating the trained models ...

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

Energy-Aware Decentralized Learning with Intermittent Model Training

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

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

Search for high-mass exclusive diphoton production with tagged protons in proton-proton collisions at √s=13 TeV

Matthias Finger, Qian Wang, Varun Sharma, Konstantin Androsov, Jan Steggemann, Ian Smith, Xin Chen, Rakesh Chawla, Michele De Palma, Matteo Galli

A search is presented for high-mass exclusive diphoton production via photon-photon fusion in proton-proton collisions at root s = 13 TeV in events where both protons survive the interaction. The analysis utilizes data corresponding to an integrated lumino ...

AMER PHYSICAL SOC2024

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