66 citations · 116 across the 18 of their papers we have counts for
8 papers · 1 filter
Who Leaked the Model? Tracking IP Infringers in Accountable Federated Learning
Shuyang Yu, Junyuan Hong, Yi Zeng +3
Federated learning (FL) emerges as an effective collaborative learning framework to coordinate data and computation resources from massive and distributed clients in training. Such…
DP-OPT: Make Large Language Model Your Privacy-Preserving Prompt Engineer
Junyuan Hong, Jiachen T. Wang, Chenhui Zhang +3
Large Language Models (LLMs) have emerged as dominant tools for various tasks, particularly when tailored for a specific target by prompt tuning. Nevertheless, concerns surrounding…
Understanding Deep Gradient Leakage via Inversion Influence Functions
Haobo Zhang, Junyuan Hong, Yuyang Deng +2
Deep Gradient Leakage (DGL) is a highly effective attack that recovers private training images from gradient vectors. This attack casts significant privacy challenges on distribute…
Safe and Robust Watermark Injection with a Single OoD Image
Shuyang Yu, Junyuan Hong, Haobo Zhang +3
Training a high-performance deep neural network requires large amounts of data and computational resources. Protecting the intellectual property (IP) and commercial ownership of a…
Revisiting Data-Free Knowledge Distillation with Poisoned Teachers
Junyuan Hong, Yi Zeng, Shuyang Yu +3
Data-free knowledge distillation (KD) helps transfer knowledge from a pre-trained model (known as the teacher model) to a smaller model (known as the student model) without access…
FedNoisy: Federated Noisy Label Learning Benchmark
Siqi Liang, Jintao Huang, Junyuan Hong +3
Federated learning has gained popularity for distributed learning without aggregating sensitive data from clients. But meanwhile, the distributed and isolated nature of data isolat…
On the Hardness of Robustness Transfer: A Perspective from Rademacher Complexity over Symmetric Difference Hypothesis Space
Yuyang Deng, Nidham Gazagnadou, Junyuan Hong +2
Recent studies demonstrated that the adversarially robust learning under attack is harder to generalize to different domains than standard domain adaptation. How to t…
A Privacy-Preserving Hybrid Federated Learning Framework for Financial Crime Detection
Haobo Zhang, Junyuan Hong, Fan Dong +3
The recent decade witnessed a surge of increase in financial crimes across the public and private sectors, with an average cost of scams of $102m to financial institutions in 2022.…