Search Results for author:
Found 5 papers, 2 papers with code
An Experimental Study of Byzantine-Robust Aggregation Schemes in Federated Learning
In recent years, several robust aggregation schemes have been proposed to defend against malicious updates from Byzantine clients and improve the robustness of federated learning.
On-device Training: A First Overview on Existing Systems
A majority of the early application systems focused on exploiting the inference capabilities of ML and DL models, where data captured from different mobile and embedded sensing components are processed through these models for application goals such as classification and segmentation.
DeepGANTT: A Scalable Deep Learning Scheduler for Backscatter Networks
Without the need to retrain, DeepGANTT generalizes to networks 6x larger in the number of nodes and 10x larger in the number of tags than those used for training, breaking the scalability limitations of the optimal scheduler and reducing carrier utilization by up to 50% compared to the state-of-the-art heuristic.
Auto-weighted Robust Federated Learning with Corrupted Data Sources
In this paper, we address this challenge by proposing Auto-weighted Robust Federated Learning (arfl), a novel approach that jointly learns the global model and the weights of local updates to provide robustness against corrupted data sources.
The Dark (and Bright) Side of IoT: Attacks and Countermeasures for Identifying Smart Home Devices and Services
We present a new machine learning-based attack that exploits network patterns to detect the presence of smart IoT devices and running services in the WiFi radio spectrum.
