109 citations · 329 across the 27 of their papers we have counts for
28 papers
A Large-scale Examination of "Socioeconomic" Fairness in Mobile Networks
Souneil Park, Pavol Mulinka, Diego Perino
Internet access is a special resource of which needs has become universal across the public whereas the service is operated in the private sector. Mobile Network Operators (MNOs) p…
A Survey on Approximate Edge AI for Energy Efficient Autonomous Driving Services
Dewant Katare, Diego Perino, Jari Nurmi +3
Autonomous driving services rely heavily on sensors such as cameras, LiDAR, radar, and communication modules. A common practice of processing the sensed data is using a high-perfor…
P4L: Privacy Preserving Peer-to-Peer Learning for Infrastructureless Setups
Ioannis Arapakis, Panagiotis Papadopoulos, Kleomenis Katevas +1
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 diffe…
Scheduling Inference Workloads on Distributed Edge Clusters with Reinforcement Learning
Gabriele Castellano, Juan-José Nieto, Jordi Luque +6
Many real-time applications (e.g., Augmented/Virtual Reality, cognitive assistance) rely on Deep Neural Networks (DNNs) to process inference tasks. Edge computing is considered a k…
Hierarchical Federated Learning with Privacy
Varun Chandrasekaran, Suman Banerjee, Diego Perino +1
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. Recent work d…
FLaaS: Cross-App On-device Federated Learning in Mobile Environments
Kleomenis Katevas, Diego Perino, Nicolas Kourtellis
Federated Learning (FL) has recently emerged as a popular solution to distributedly train a model on user devices improving user privacy and system scalability. Major Internet comp…