1 citations · 1 across the 11 of their papers we have counts for
12 papers
Safety Anchor: Defending Harmful Fine-tuning via Geometric Bottlenecks
Guoxin Lu, Letian Sha, Qing Wang +4
The safety alignment of Large Language Models (LLMs) remains vulnerable to Harmful Fine-tuning (HFT). While existing defenses impose constraints on parameters, gradients, or intern…
Conceal, Reconstruct, Jailbreak: Exploiting the Reconstruction-Concealment Tradeoff in MLLMs
Md Farhamdur Reza, Richeng Jin, Tianfu Wu +1
Intent-obfuscation-based jailbreak attacks on multimodal large language models (MLLMs) transform a harmful query into a concealed multimodal input to bypass safety mechanisms. We s…
Gradient Compression May Hurt Generalization: A Remedy by Synthetic Data Guided Sharpness Aware Minimization
Yujie Gu, Richeng Jin, Zhaoyang Zhang +1
It is commonly believed that gradient compression in federated learning (FL) enjoys significant improvement in communication efficiency with negligible performance degradation. In…
Mobility-Assisted Decentralized Federated Learning: Convergence Analysis and A Data-Driven Approach
Reza Jahani, Md Farhamdur Reza, Richeng Jin +1
Decentralized Federated Learning (DFL) has emerged as a privacy-preserving machine learning paradigm that enables collaborative training among users without relying on a central se…
Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates
Kai Yue, Richeng Jin, Chau-Wai Wong +1
Federated learning (FL) enables decentralized machine learning without sharing raw data, allowing multiple clients to collaboratively learn a global model. However, studies reveal…
Computation- and Communication-Efficient Online FL for Resource-Constrained Aerial Vehicles
Ferdous Pervej, Richeng Jin, Md Moin Uddin Chowdhury +3
Privacy-preserving distributed machine learning (ML) and aerial connected vehicle (ACV)-assisted edge computing have drawn significant attention lately. Since the onboard sensors o…
Distribution-Aware Mobility-Assisted Decentralized Federated Learning
Md Farhamdur Reza, Reza Jahani, Richeng Jin +1
Decentralized federated learning (DFL) has attracted significant attention due to its scalability and independence from a central server. In practice, some participating clients ca…
GSBA: - Geometric Score-based Black-box Attack
Md Farhamdur Reza, Richeng Jin, Tianfu Wu +1
Existing score-based adversarial attacks mainly focus on crafting -1 adversarial examples against classifiers with single-label classification. Their attack success rate and q…
Federated Learning Nodes Can Reconstruct Peers' Image Data
Ethan Wilson, Kai Yue, Chau-Wai Wong +1
Federated learning (FL) is a privacy-preserving machine learning framework that enables multiple nodes to train models on their local data and periodically average weight updates t…
NTK-DFL: Enhancing Decentralized Federated Learning in Heterogeneous Settings via Neural Tangent Kernel
Gabriel Thompson, Kai Yue, Chau-Wai Wong +1
Decentralized federated learning (DFL) is a collaborative machine learning framework for training a model across participants without a central server or raw data exchange. DFL fac…
Advancing Hybrid Defense for Byzantine Attacks in Federated Learning
Kai Yue, Richeng Jin, Chau-Wai Wong +1
Federated learning (FL) enables multiple clients to collaboratively train a global model without sharing their local data. Recent studies have highlighted the vulnerability of FL t…
Individualized Deepfake Detection Exploiting Traces Due to Double Neural-Network Operations
Mushfiqur Rahman, Runze Liu, Chau-Wai Wong +1
In today's digital landscape, journalists urgently require tools to verify the authenticity of facial images and videos depicting specific public figures before incorporating them…