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Found 7 papers, 2 papers with code
Sample Selection with Deadline Control for Efficient Federated Learning on Heterogeneous Clients
Federated Learning (FL) trains a machine learning model on distributed clients without exposing individual data.
Zero-Shot Certified Defense against Adversarial Patches with Vision Transformers
Such attacks are a major threat to models deployed in the physical world, as they can be easily realized by presenting a customized object in the camera view.
DistFL: Distribution-aware Federated Learning for Mobile Scenarios
Previous studies focus on the "symptoms" directly, as they try to improve the accuracy or detect possible attacks by adding extra steps to conventional FL models.
Rethinking the Representational Continuity: Towards Unsupervised Continual Learning
In this work, we focus on unsupervised continual learning (UCL), where we learn the feature representations on an unlabelled sequence of tasks and show that reliance on annotated data is not necessary for continual learning.
ModelDiff: Testing-Based DNN Similarity Comparison for Model Reuse Detection
The knowledge of a deep learning model may be transferred to a student model, leading to intellectual property infringement or vulnerability propagation.
DeepPayload: Black-box Backdoor Attack on Deep Learning Models through Neural Payload Injection
The core of the attack is a neural conditional branch constructed with a trigger detector and several operators and injected into the victim model as a malicious payload.
Beyond the Virus: A First Look at Coronavirus-themed Mobile Malware
To facilitate future research, we have publicly released all the well-labelled COVID-19 themed apps (and malware) to the research community.
Cryptography and Security
