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Found 16 papers, 6 papers with code
Model Extraction Attacks on Split Federated Learning
We show that under practical cases, the proposed ME attacks work exceptionally well for SFL.
Proactively Predicting Dynamic 6G Link Blockages Using LiDAR and In-Band Signatures
Given this future blockage prediction capability, the paper also shows that the developed solutions can achieve an order of magnitude saving in network latency, which further highlights the potential of the developed blockage prediction solutions for wireless networks.
An Adjustable Farthest Point Sampling Method for Approximately-sorted Point Cloud Data
Sampling is an essential part of raw point cloud data processing such as in the popular PointNet++ scheme.
ResSFL: A Resistance Transfer Framework for Defending Model Inversion Attack in Split Federated Learning
While such a scheme helps reduce the computational load at the client end, it opens itself to reconstruction of raw data from intermediate activation by the server.
LiDAR-Aided Mobile Blockage Prediction in Real-World Millimeter Wave Systems
If used for proactive hand-off, the proposed solutions can potentially provide an order of magnitude saving in the network latency, which highlights a promising direction for addressing the blockage challenges in mmWave/sub-THz networks.
Blockage Prediction Using Wireless Signatures: Deep Learning Enables Real-World Demonstration
In this paper, we propose a novel solution that relies only on in-band mmWave wireless measurements to proactively predict future dynamic line-of-sight (LOS) link blockages.
SIAM: Chiplet-based Scalable In-Memory Acceleration with Mesh for Deep Neural Networks
In-memory computing (IMC) on a monolithic chip for deep learning faces dramatic challenges on area, yield, and on-chip interconnection cost due to the ever-increasing model sizes.
Communication and Computation Reduction for Split Learning using Asynchronous Training
An added benefit of the proposed communication reduction method is that the computations at the client side are reduced due to reduction in the number of client model updates.
Impact of On-Chip Interconnect on In-Memory Acceleration of Deep Neural Networks
In this technique, we use analytical models of NoC to evaluate end-to-end communication latency of any given DNN.
RA-BNN: Constructing Robust & Accurate Binary Neural Network to Simultaneously Defend Adversarial Bit-Flip Attack and Improve Accuracy
Apart from recovering the inference accuracy, our RA-BNN after growing also shows significantly higher resistance to BFA.
RADAR: Run-time Adversarial Weight Attack Detection and Accuracy Recovery
In this work, we propose RADAR, a Run-time adversarial weight Attack Detection and Accuracy Recovery scheme to protect DNN weights against PBFA.
Deep Learning for Moving Blockage Prediction using Real Millimeter Wave Measurements
To evaluate our proposed approach, we build a mmWave communication setup with a moving blockage and collect a dataset of received power sequences.
T-BFA: Targeted Bit-Flip Adversarial Weight Attack
Prior works of BFA focus on un-targeted attack that can hack all inputs into a random output class by flipping a very small number of weight bits stored in computer memory.
Automated Parallel Kernel Extraction from Dynamic Application Traces
Modern program runtime is dominated by segments of repeating code called kernels.
Distributed, Parallel, and Cluster Computing Programming Languages
Minimizing Area and Energy of Deep Learning Hardware Design Using Collective Low Precision and Structured Compression
Deep learning algorithms have shown tremendous success in many recognition tasks; however, these algorithms typically include a deep neural network (DNN) structure and a large number of parameters, which makes it challenging to implement them on power/area-constrained embedded platforms.
Algorithm and Hardware Design of Discrete-Time Spiking Neural Networks Based on Back Propagation with Binary Activations
We present a new back propagation based training algorithm for discrete-time spiking neural networks (SNN).
