57 citations · 93 across the 6 of their papers we have counts for
6 papers
Mitigating Adversarial Attacks in Federated Learning with Trusted Execution Environments
Simon Queyrut, Valerio Schiavoni, Pascal Felber
The main premise of federated learning (FL) is that machine learning model updates are computed locally to preserve user data privacy. This approach avoids by design user data to e…
Pelta: Shielding Transformers to Mitigate Evasion Attacks in Federated Learning
Simon Queyrut, Yérom-David Bromberg, Valerio Schiavoni
The main premise of federated learning is that machine learning model updates are computed locally, in particular to preserve user data privacy, as those never leave the perimeter…
NVMM cache design: Logging vs. Paging
Rémi Dulong, Quentin Acher, Baptiste Lepers +3
Modern NVMM is closing the gap between DRAM and persistent storage, both in terms of performance and features. Having both byte addressability and persistence on the same device gi…
Shielding Federated Learning Systems against Inference Attacks with ARM TrustZone
Aghiles Ait Messaoud, Sonia Ben Mokhtar, Vlad Nitu +1
Federated Learning (FL) opens new perspectives for training machine learning models while keeping personal data on the users premises. Specifically, in FL, models are trained on th…
VEDLIoT: Very Efficient Deep Learning in IoT
Martin Kaiser, Rene Griessl, Nils Kucza +33
The VEDLIoT project targets the development of energy-efficient Deep Learning for distributed AIoT applications. A holistic approach is used to optimize algorithms while also deali…
WebAssembly as a Common Layer for the Cloud-edge Continuum
Jämes Ménétrey, Marcelo Pasin, Pascal Felber +1
Over the last decade, the cloud computing landscape has transformed from a centralised architecture made of large data centres to a distributed and heterogeneous architecture embra…