Publications by Akash Balasaheb Dhasade

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

Practical Federated Learning without a Server

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

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

Fairness Auditing with Multi-Agent Collaboration

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

Existing work in fairness auditing assumes that each audit is performed independently. In this paper, we consider multiple agents working together, each auditing the same platform for different tasks. Agents have two levers: their collaboration strategy, w ...

IOS Press2024

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

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

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