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Found 9 papers, 0 papers with code
AnoFel: Supporting Anonymity for Privacy-Preserving Federated Learning
In this paper, we introduce AnoFel, the first framework to support private and anonymous dynamic participation in federated learning.
zPROBE: Zero Peek Robustness Checks for Federated Learning
However, keeping the individual updates private allows malicious users to perform Byzantine attacks and degrade the accuracy without being detected.
Sphynx: ReLU-Efficient Network Design for Private Inference
The emergence of deep learning has been accompanied by privacy concerns surrounding users' data and service providers' models.
Circa: Stochastic ReLUs for Private Deep Learning
In this paper we re-think the ReLU computation and propose optimizations for PI tailored to properties of neural networks.
Generating and Characterizing Scenarios for Safety Testing of Autonomous Vehicles
Extracting interesting scenarios from real-world data as well as generating failure cases is important for the development and testing of autonomous systems.
DeepReDuce: ReLU Reduction for Fast Private Inference
This paper proposes DeepReDuce: a set of optimizations for the judicious removal of ReLUs to reduce private inference latency.
CryptoNAS: Private Inference on a ReLU Budget
Machine learning as a service has given raise to privacy concerns surrounding clients' data and providers' models and has catalyzed research in private inference (PI): methods to process inferences without disclosing inputs.
ThUnderVolt: Enabling Aggressive Voltage Underscaling and Timing Error Resilience for Energy Efficient Deep Neural Network Accelerators
Hardware accelerators are being increasingly deployed to boost the performance and energy efficiency of deep neural network (DNN) inference.
SafetyNets: Verifiable Execution of Deep Neural Networks on an Untrusted Cloud
Specifically, SafetyNets develops and implements a specialized interactive proof (IP) protocol for verifiable execution of a class of deep neural networks, i. e., those that can be represented as arithmetic circuits.
