15 citations · 31 across the 8 of their papers we have counts for
8 papers
Energy-Aware Decentralized Learning with Intermittent Model Training
Akash Dhasade, Paolo Dini, Elia Guerra +5
Decentralized learning (DL) offers a powerful framework where nodes collaboratively train models without sharing raw data and without the coordination of a central server. In the i…
Fairness Auditing with Multi-Agent Collaboration
Martijn de Vos, Akash Dhasade, Jade Garcia Bourrée +4
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…
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…
Decentralized Learning Made Practical with Client Sampling
Martijn de Vos, Akash Dhasade, Anne-Marie Kermarrec +3
Decentralized learning (DL) leverages edge devices for collaborative model training while avoiding coordination by a central server. Due to privacy concerns, DL has become an attra…
TEE-based decentralized recommender systems: The raw data sharing redemption
Akash Dhasade, Nevena Dresevic, Anne-Marie Kermarrec +1
Recommenders are central in many applications today. The most effective recommendation schemes, such as those based on collaborative filtering (CF), exploit similarities between us…