6 citations · 16 across the 7 of their papers we have counts for
7 papers
Integrating Homomorphic Encryption and Synthetic Data in FL for Privacy and Learning Quality
Yenan Wang, Carla Fabiana Chiasserini, Elad Michael Schiller
Federated learning (FL) enables collaborative training of machine learning models without sharing sensitive client data, making it a cornerstone for privacy-critical applications.…
MOSE: A Novel Orchestration Framework for Stateful Microservice Migration at the Edge
Antonio Calagna, Yenchia Yu, Paolo Giaccone +1
Stateful migration has emerged as the dominant technology to support microservice mobility at the network edge while ensuring a satisfying experience to mobile end users. This work…
Sharing GPUs and Programmable Switches in a Federated Testbed with SHARY
Stefano Salsano, Andrea Mayer, Paolo Lungaroni +8
Federated testbeds enable collaborative research by providing access to diverse resources, including computing power, storage, and specialized hardware like GPUs, programmable swit…
Resource-Efficient Sensor Fusion via System-Wide Dynamic Gated Neural Networks
Chetna Singhal, Yashuo Wu, Francesco Malandrino +3
Mobile systems will have to support multiple AI-based applications, each leveraging heterogeneous data sources through DNN architectures collaboratively executed within the network…
Matching DNN Compression and Cooperative Training with Resources and Data Availability
Francesco Malandrino, Giuseppe Di Giacomo, Armin Karamzade +2
To make machine learning (ML) sustainable and apt to run on the diverse devices where relevant data is, it is essential to compress ML models as needed, while still meeting the req…
Characterizing the Computational and Memory Requirements of Virtual RANs
Somreeta Pramanik, Adlen Ksentini, Carla Fabiana Chiasserini
The virtualization of radio access networks (RANs) is emerging as a key component of future wireless systems, as it brings agility to the RAN architecture and offers degrees of des…