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Found 29 papers, 23 papers with code
On the Efficiency of Privacy Attacks in Federated Learning
Second, we propose three early-stopping techniques to effectively reduce the computational costs of these privacy attacks.
Security and Privacy Challenges of Large Language Models: A Survey
We assess the extent of LLM vulnerabilities, investigate emerging security and privacy attacks for LLMs, and review the potential defense mechanisms.
Hierarchical Pruning of Deep Ensembles with Focal Diversity
This paper presents a novel deep ensemble pruning approach, which can efficiently identify smaller deep ensembles and provide higher ensemble accuracy than the entire deep ensemble of a large number of member networks.
Privacy Risks Analysis and Mitigation in Federated Learning for Medical Images
Federated learning (FL) is gaining increasing popularity in the medical domain for analyzing medical images, which is considered an effective technique to safeguard sensitive patient data and comply with privacy regulations.
Exploring Model Learning Heterogeneity for Boosting Ensemble Robustness
We show that this two-tier heterogeneity driven ensemble construction method can compose an ensemble team that promotes high ensemble diversity and low negative correlation among member models of the ensemble, strengthening ensemble robustness against both negative examples and adversarial attacks.
Invisible Watermarking for Audio Generation Diffusion Models
Diffusion models have gained prominence in the image domain for their capabilities in data generation and transformation, achieving state-of-the-art performance in various tasks in both image and audio domains.
Rethinking Learning Rate Tuning in the Era of Large Language Models
Second, we present LRBench++ to benchmark learning rate policies and facilitate learning rate tuning for both traditional DNNs and LLMs.
Fast and Resource-Efficient Object Tracking on Edge Devices: A Measurement Study
Object tracking is an important functionality of edge video analytic systems and services.
Securing Distributed SGD against Gradient Leakage Threats
Next, we present a gradient leakage resilient approach to securing distributed SGD in federated learning, with differential privacy controlled noise as the tool.
STDLens: Model Hijacking-Resilient Federated Learning for Object Detection
Based on the insights, we introduce a three-tier forensic framework to identify and expel Trojaned gradients and reclaim the performance over the course of FL.
Adaptive Deep Neural Network Inference Optimization with EENet
Instead of having every sample go through all DNN layers during prediction, EENet learns an early exit scheduler, which can intelligently terminate the inference earlier for certain predictions, which the model has high confidence of early exit.
Selecting and Composing Learning Rate Policies for Deep Neural Networks
First, we develop an LR tuning mechanism for auto-verification of a given LR policy with respect to the desired accuracy goal under the pre-defined training time constraint.
Boosting Deep Ensemble Performance with Hierarchical Pruning
Evaluated using two benchmark datasets, we show that the proposed focal diversity powered hierarchical pruning can find significantly smaller ensembles of deep neural network models while achieving the same or better classification generalizability.
Learning Text-Image Joint Embedding for Efficient Cross-Modal Retrieval with Deep Feature Engineering
This paper introduces a two-phase deep feature engineering framework for efficient learning of semantics enhanced joint embedding, which clearly separates the deep feature engineering in data preprocessing from training the text-image joint embedding model.
Learning TFIDF Enhanced Joint Embedding for Recipe-Image Cross-Modal Retrieval Service
We present a Multi-modal Semantics enhanced Joint Embedding approach (MSJE) for learning a common feature space between the two modalities (text and image), with the ultimate goal of providing high-performance cross-modal retrieval services.
Parallel Detection for Efficient Video Analytics at the Edge
A common performance requirement in these mission-critical edge services is the near real-time latency of online object detection on edge devices.
Gradient-Leakage Resilient Federated Learning
This paper presents a gradient leakage resilient approach to privacy-preserving federated learning with per training example-based client differential privacy, coined as Fed-CDP.
Boosting Ensemble Accuracy by Revisiting Ensemble Diversity Metrics
Our new metrics significantly improve the intrinsic correlation between high ensemble diversity and high ensemble accuracy.
Deep Ensembles with Hierarchical Diversity Pruning
(3) We introduce a two phase hierarchical pruning method to effectively identify and prune those deep ensembles with high HQ diversity scores, aiming to increase the lower and upper bounds on ensemble accuracy for the selected ensembles.
Promoting High Diversity Ensemble Learning with EnsembleBench
Ensemble learning is gaining renewed interests in recent years.
Understanding Object Detection Through An Adversarial Lens
We demonstrate that the proposed framework can serve as a methodical benchmark for analyzing adversarial behaviors and risks in real-time object detection systems.
A Framework for Evaluating Gradient Leakage Attacks in Federated Learning
FL offers default client privacy by allowing clients to keep their sensitive data on local devices and to only share local training parameter updates with the federated server.
TOG: Targeted Adversarial Objectness Gradient Attacks on Real-time Object Detection Systems
The rapid growth of real-time huge data capturing has pushed the deep learning and data analytic computing to the edge systems.
Cross-Layer Strategic Ensemble Defense Against Adversarial Examples
Deep neural network (DNN) has demonstrated its success in multiple domains.
Deep Neural Network Ensembles against Deception: Ensemble Diversity, Accuracy and Robustness
In this paper we first give an overview of the concept of ensemble diversity and examine the three types of ensemble diversity in the context of DNN classifiers.
Denoising and Verification Cross-Layer Ensemble Against Black-box Adversarial Attacks
Deep neural networks (DNNs) have demonstrated impressive performance on many challenging machine learning tasks.
Demystifying Learning Rate Policies for High Accuracy Training of Deep Neural Networks
Learning Rate (LR) is an important hyper-parameter to tune for effective training of deep neural networks (DNNs).
A Comparative Measurement Study of Deep Learning as a Service Framework
Big data powered Deep Learning (DL) and its applications have blossomed in recent years, fueled by three technological trends: a large amount of digitized data openly accessible, a growing number of DL software frameworks in open source and commercial markets, and a selection of affordable parallel computing hardware devices.
Adversarial Examples in Deep Learning: Characterization and Divergence
The burgeoning success of deep learning has raised the security and privacy concerns as more and more tasks are accompanied with sensitive data.
