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06/20/2023
DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models
Generative Pre-trained Transformer (GPT) models have exhibited exciting ...
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06/08/2023
FedMLSecurity: A Benchmark for Attacks and Defenses in Federated Learning and LLMs
This paper introduces FedMLSecurity, a benchmark that simulates adversar...
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02/13/2023
PerAda: Parameter-Efficient and Generalizable Federated Learning Personalization with Guarantees
Personalized Federated Learning (pFL) has emerged as a promising solutio...
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09/08/2022
Uncovering the Connection Between Differential Privacy and Certified Robustness of Federated Learning against Poisoning Attacks
Federated learning (FL) provides an efficient paradigm to jointly train ...
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09/08/2022
Privacy of Autonomous Vehicles: Risks, Protection Methods, and Future Directions
Recent advances in machine learning have enabled its wide application in...
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07/21/2022
UniFed: A Benchmark for Federated Learning Frameworks
Federated Learning (FL) has become a practical and popular paradigm in m...
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07/21/2022
FOCUS: Fairness via Agent-Awareness for Federated Learning on Heterogeneous Data
Federated learning (FL) provides an effective paradigm to train machine ...
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07/20/2022
Improving Privacy-Preserving Vertical Federated Learning by Efficient Communication with ADMM
Federated learning (FL) enables distributed devices to jointly train a s...
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07/15/2021
Subnet Replacement: Deployment-stage backdoor attack against deep neural networks in gray-box setting
We study the realistic potential of conducting backdoor attack against d...
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06/15/2021
CRFL: Certifiably Robust Federated Learning against Backdoor Attacks
Federated Learning (FL) as a distributed learning paradigm that aggregat...
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03/03/2021
Style-based Point Generator with Adversarial Rendering for Point Cloud Completion
In this paper, we proposed a novel Style-based Point Generator with Adve...
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12/18/2020
Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses
As machine learning systems grow in scale, so do their training data req...
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12/24/2019