Search Results for author:
Found 7 papers, 1 papers with code
A Survey on Approximate Edge AI for Energy Efficient Autonomous Driving Services
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.
P4L: Privacy Preserving Peer-to-Peer Learning for Infrastructureless Setups
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).
Scheduling Inference Workloads on Distributed Edge Clusters with Reinforcement Learning
Many real-time applications (e. g., Augmented/Virtual Reality, cognitive assistance) rely on Deep Neural Networks (DNNs) to process inference tasks.
Hierarchical Federated Learning with Privacy
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.
PPFL: Privacy-preserving Federated Learning with Trusted Execution Environments
We propose and implement a Privacy-preserving Federated Learning ($PPFL$) framework for mobile systems to limit privacy leakages in federated learning.
Back in control -- An extensible middle-box on your phone
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
FLaaS: Federated Learning as a Service
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.
