15 citations · 31 across the 8 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…
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…
Boosting Resource-Constrained Federated Learning Systems with Guessed Updates
Mohamed Yassine Boukhari, Akash Dhasade, Anne-Marie Kermarrec +3
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…