no code implementations • 13 Apr 2023 • Dewant Katare, Diego Perino, Jari Nurmi, Martijn Warnier, Marijn Janssen, Aaron Yi Ding
The insights and vision from this survey can be beneficial for the collaborative driving service development on low-power and memory-constrained systems and also for the energy optimization of autonomous vehicles.
no code implementations • 26 Feb 2023 • Ioannis Arapakis, Panagiotis Papadopoulos, Kleomenis Katevas, Diego Perino
Distributed (or Federated) learning enables users to train machine learning models on their very own devices, while they share only the gradients of their models usually in a differentially private way (utility loss).
no code implementations • 31 Jan 2023 • Gabriele Castellano, Juan-José Nieto, Jordi Luque, Ferrán Diego, Carlos Segura, Diego Perino, Flavio Esposito, Fulvio Risso, Aravindh Raman
Many real-time applications (e. g., Augmented/Virtual Reality, cognitive assistance) rely on Deep Neural Networks (DNNs) to process inference tasks.
no code implementations • 10 Jun 2022 • Varun Chandrasekaran, Suman Banerjee, Diego Perino, Nicolas Kourtellis
Federated learning (FL), where data remains at the federated clients, and where only gradient updates are shared with a central aggregator, was assumed to be private.
1 code implementation • 29 Apr 2021 • Fan Mo, Hamed Haddadi, Kleomenis Katevas, Eduard Marin, Diego Perino, Nicolas Kourtellis
We propose and implement a Privacy-preserving Federated Learning ($PPFL$) framework for mobile systems to limit privacy leakages in federated learning.
no code implementations • 14 Dec 2020 • James Newman, Abbas Razaghpanah, Narseo Vallina-Rodriguez, Fabian E. Bustamante, Mark Allman, Diego Perino, Alessandro Finamore
The closed design of mobile devices -- with the increased security and consistent user interfaces -- is in large part responsible for their becoming the dominant platform for accessing the Internet.
Networking and Internet Architecture
no code implementations • 18 Nov 2020 • Nicolas Kourtellis, Kleomenis Katevas, Diego Perino
Indeed, FL enables local training on user devices, avoiding user data to be transferred to centralized servers, and can be enhanced with differential privacy mechanisms.