10 citations · 26 across the 18 of their papers we have counts for
3 papers · 1 filter
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…
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…
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…