Publications (57)
Jailbreak-as-a-Service++: Unveiling Distributed AI-Driven Malicious Information Campaigns Powered by LLM Crowdsourcing
Yu Yan, Sheng Sun, Mingfeng Li +6
To prevent the misuse of Large Language Models (LLMs) for malicious purposes, numerous efforts have been made to develop the safety alignment mechanisms of LLMs. However, as multip…
Towards 5G Zero Trusted Air Interface Architecture
Sheng Sun, Morris Repeta, Mike Healy +3
5G is destined to be supporting large deployment of Industrial IoT (IIoT) with the characteristics of ultra-high densification and low latency. 5G utilizes a more intelligent archi…
TransDiffuser: Diverse Trajectory Generation with Decorrelated Multi-modal Representation for End-to-end Autonomous Driving
Xuefeng Jiang, Yuan Ma, Pengxiang Li +7
In recent years, diffusion models have demonstrated remarkable potential across diverse domains, from vision generation to language modeling. Transferring its generative capabiliti…
Learnable Sparse Customization in Heterogeneous Edge Computing
Jingjing Xue, Sheng Sun, Min Liu +3
To effectively manage and utilize massive distributed data at the network edge, Federated Learning (FL) has emerged as a promising edge computing paradigm across data silos. Howeve…
Non-iterative Methods in Inhomogeneous Background Inverse Scattering Imaging Problem Assisted by Swin Transformer Network
Naike Du, Tiantian Yin, Jing Wang +5
A deep learning-assisted inversion method is proposed to solve the inhomogeneous background imaging problem. Three non-iterative methods, namely the distorted-Born (DB) major curre…
Towards Federated Learning against Noisy Labels via Local Self-Regularization
Xuefeng Jiang, Sheng Sun, Yuwei Wang +1
Federated learning (FL) aims to learn joint knowledge from a large scale of decentralized devices with labeled data in a privacy-preserving manner. However, since high-quality labe…
GDformer: Going Beyond Subsequence Isolation for Multivariate Time Series Anomaly Detection
Qingxiang Liu, Xiaoliang Luo, Chenghao Liu +5
Unsupervised anomaly detection of multivariate time series is a challenging task, given the requirements of deriving a compact detection criterion without accessing the anomaly poi…
Logits Poisoning Attack in Federated Distillation
Yuhan Tang, Zhiyuan Wu, Bo Gao +3
Federated Distillation (FD) is a novel and promising distributed machine learning paradigm, where knowledge distillation is leveraged to facilitate a more efficient and flexible cr…
FoMoVLA: Bridging Visual Foresight and Motion Guidance for Vision-Language-Action Models
Wei Li, Peijin Jia, Yuan Ma +9
FoMoVLA enhances vision-language-action models by jointly predicting future visual features and tracking sparse 2D points, providing both goal states and motion paths to improve co…
Privacy-Enhanced Training-as-a-Service for On-Device Intelligence: Concept, Architectural Scheme, and Open Problems
Zhiyuan Wu, Sheng Sun, Yuwei Wang +4
On-device intelligence (ODI) enables artificial intelligence (AI) applications to run on end devices, providing real-time and customized AI inference without relying on remote serv…
Breaking the Degrees-of-Freedom Limit of Holographic MIMO Communications: A 3-D Antenna Array Topology
Shuai S. A. Yuan, Jie Wu, Hongjing Xu +9
The performance of holographic multiple-input multiple-output (MIMO) communications, employing two-dimensional (2-D) planar antenna arrays, is typically compromised by finite degre…
Personalized Federated Learning for Spatio-Temporal Forecasting: A Dual Semantic Alignment-Based Contrastive Approach
Qingxiang Liu, Sheng Sun, Yuxuan Liang +2
The existing federated learning (FL) methods for spatio-temporal forecasting fail to capture the inherent spatio-temporal heterogeneity, which calls for personalized FL (PFL) metho…
Red-teaming the Multimodal Reasoning: Jailbreaking Vision-Language Models via Cross-modal Entanglement Attacks
Yu Yan, Sheng Sun, Shengjia Cheng +3
Vision-Language Models (VLMs) with multimodal reasoning capabilities are high-value attack targets, given their potential for handling complex multimodal harmful tasks. Mainstream…
Approaching the Fundamental Limit of Orbital Angular Momentum Multiplexing Through a Hologram Metasurface
Shuai S. A. Yuan, Jie Wu, Menglin L. N. Chen +9
Establishing and approaching the fundamental limit of orbital angular momentum (OAM) multiplexing are necessary and increasingly urgent for current multiple-input multiple-output r…
Collaborative Stance Detection via Small-Large Language Model Consistency Verification
Yu Yan, Sheng Sun, Zixiang Tang +2
Stance detection on social media aims to identify attitudes expressed in tweets towards specific targets. Current studies prioritize Large Language Models (LLMs) over Small Languag…
Federated Class-Incremental Learning with New-Class Augmented Self-Distillation
Zhiyuan Wu, Tianliu He, Sheng Sun +4
Federated Learning (FL) enables collaborative model training among participants while guaranteeing the privacy of raw data. Mainstream FL methodologies overlook the dynamic nature…
Seeing is Believing: Robust Vision-Guided Cross-Modal Prompt Learning under Label Noise
Zibin Geng, Xuefeng Jiang, Jia Li +6
Prompt learning is a parameter-efficient approach for vision-language models, yet its robustness under label noise is less investigated. Visual content contains richer and more rel…
FNBench: Benchmarking Robust Federated Learning against Noisy Labels
Xuefeng Jiang, Jia Li, Nannan Wu +7
Robustness to label noise within data is a significant challenge in federated learning (FL). From the data-centric perspective, the data quality of distributed datasets can not be…
FedCache 2.0: Federated Edge Learning with Knowledge Caching and Dataset Distillation
Quyang Pan, Sheng Sun, Zhiyuan Wu +4
Federated Edge Learning (FEL) has emerged as a promising approach for enabling edge devices to collaboratively train machine learning models while preserving data privacy. Despite…
Knowledge Distillation in Federated Edge Learning: A Survey
Zhiyuan Wu, Sheng Sun, Yuwei Wang +4
The increasing demand for intelligent services and privacy protection of mobile and Internet of Things (IoT) devices motivates the wide application of Federated Edge Learning (FEL)…
Confusion is the Final Barrier: Rethinking Jailbreak Evaluation and Investigating the Real Misuse Threat of LLMs
Yu Yan, Sheng Sun, Zhe Wang +6
With the development of Large Language Models (LLMs), numerous efforts have revealed their vulnerabilities to jailbreak attacks. Although these studies have driven the progress in…
Robust Federated Learning against Noisy Clients via Masked Optimization
Xuefeng Jiang, Tian Wen, Zhiqin Yang +5
In recent years, federated learning (FL) has made significant advance in privacy-sensitive applications. However, it can be hard to ensure that FL participants provide well-annotat…
Improving Communication Efficiency of Federated Distillation via Accumulating Local Updates
Zhiyuan Wu, Sheng Sun, Yuwei Wang +3
As an emerging federated learning paradigm, federated distillation enables communication-efficient model training by transmitting only small-scale knowledge during the learning pro…
Resource-aware Probability-based Collaborative Odor Source Localization Using Multiple UAVs
Shan Wang, Sheng Sun, Min Liu +2
Benefitting from UAVs' characteristics of flexible deployment and controllable movement in 3D space, odor source localization with multiple UAVs has been a hot research area in rec…
Weakly-supervised land classification for coastal zone based on deep convolutional neural networks by incorporating dual-polarimetric characteristics into training dataset
Sheng Sun, Armando Marino, Wenze Shui +1
In this work we explore the performance of DCNNs on semantic segmentation using spaceborne polarimetric synthetic aperture radar (PolSAR) datasets. The semantic segmentation task u…
Composable Attestation: A Generalized Framework for Continuous and Incremental Trust in AI-Driven Distributed Systems
Sheng Sun, Sarah Evans
This paper presents composable attestation as a generalized cryptographic framework for Continuous and Incremental Trust in Distributed Systems,such as Artificial Intelligence (AI)…
Tackling Noisy Clients in Federated Learning with End-to-end Label Correction
Xuefeng Jiang, Sheng Sun, Jia Li +6
Recently, federated learning (FL) has achieved wide successes for diverse privacy-sensitive applications without sacrificing the sensitive private information of clients. However,…
Exploring the Distributed Knowledge Congruence in Proxy-data-free Federated Distillation
Zhiyuan Wu, Sheng Sun, Yuwei Wang +5
Federated learning (FL) is a privacy-preserving machine learning paradigm in which the server periodically aggregates local model parameters from clients without assembling their p…
Electromagnetic Effective-Degree-of-Freedom Limit of a MIMO System in 2-D Inhomogeneous Environment
Shuai S. A. Yuan, Zi He, Sheng Sun +3
Compared with a single-input-single-output (SISO) wireless communication system, the benefit of multiple-input-multiple-output (MIMO) technology originates from its extra degree of…
FedBIAD: Communication-Efficient and Accuracy-Guaranteed Federated Learning with Bayesian Inference-Based Adaptive Dropout
Jingjing Xue, Min Liu, Sheng Sun +3
Federated Learning (FL) emerges as a distributed machine learning paradigm without end-user data transmission, effectively avoiding privacy leakage. Participating devices in FL are…
Rationale-Grounded In-Context Learning for Time Series Reasoning with Multimodal Large Language Models
Qingxiang Liu, Zhiqing Cui, Xiaoliang Luo +7
The underperformance of existing multimodal large language models for time series reasoning lies in the absence of rationale priors that connect temporal observations to their down…
The role of lattice thermal conductivity suppression by dopants from a holistic perspective
Shengnan Dai, Shijie Zhang, Ye Sheng +6
Dopants play an important role in improving electrical and thermal transport. In the traditional perspective, a dopant suppresses lattice thermal conductivity kL by adding point de…
The Better You Learn, The Smarter You Prune: Towards Efficient Vision-language-action Models via Differentiable Token Pruning
Titong Jiang, Xuefeng Jiang, Yuan Ma +7
We present LightVLA, a simple yet effective differentiable token pruning framework for vision-language-action (VLA) models. While VLA models have shown impressive capability in exe…
zk-Fabric, a Polylithic Syntax Zero Knowledge Joint Proof System
Sheng Sun, Tong Wen
In this paper, we create a single-use and full syntax zero-knowledge proof system, a.k.a zk-Fabric. Comparing with zk-SNARKS and another variant zero-knowledge proofing system, zkB…
Ultra-wideband Reflection-type Metasurface for Generating Integer and Fractional Orbital Angular Momentum
Ling-Jun Yang, Sheng Sun, Wei E. I. Sha
Vortex beams carrying orbital angular momentum are extensively studied owing to its potential to expand channel capacity of microwave and optical communication. By utilizing the Pa…
Design of Wideband Microstrip Filters with Non-Equiripple Responses and Low Sensitivity
S. S. Gao, Sheng Sun
This paper presents a novel design procedure for wideband microstrip bandpass filters with non-equiripple filtering frequency responses and low sensitivity. Different from the trad…
Unraveling the Complexity of Metal Ion Dissolution: Insights from Hybrid First-Principles/Continuum Calculations
Mingqing Liu, Tong-Yi Zhang, Sheng Sun
The study of ion dissolution from metal surfaces has a long-standing history, wherein the gradual dissolution of solute atoms with increasing electrode potential, leading to their…
Agglomerative Federated Learning: Empowering Larger Model Training via End-Edge-Cloud Collaboration
Zhiyuan Wu, Sheng Sun, Yuwei Wang +5
Federated Learning (FL) enables training Artificial Intelligence (AI) models over end devices without compromising their privacy. As computing tasks are increasingly performed by a…
SearchAttack: Red-Teaming LLMs against Knowledge-to-Action Threats under Online Web Search
Yu Yan, Sheng Sun, Mingfeng Li +6
Recently, people have suffered from LLM hallucination and have become increasingly aware of the reliability gap of LLMs in open and knowledge-intensive tasks. As a result, they hav…
Manipulation of Orbital Angular Momentum Spectrum Using Shape-Tailored Metasurfaces
Ling-Jun Yang, Sheng Sun, Wei E. I. Sha
Vortex beams carrying orbital angular momentum (OAM) have been widely applied in various electromagnetic, optical, and quantum systems. A tailored OAM spectrum composed of several…
Discrete Prototypical Memories for Federated Time Series Foundation Models
Liwei Deng, Qingxiang Liu, Xinhe Niu +5
Leveraging Large Language Models (LLMs) as federated learning (FL)-based time series foundation models offers a promising way to transfer the generalization capabilities of LLMs to…
Recursive Offloading for LLM Serving in Multi-tier Networks
Zhiyuan Wu, Sheng Sun, Yuwei Wang +5
Heterogeneous device-edge-cloud computing infrastructures have become widely adopted in telecommunication operators and Wide Area Networks (WANs), offering multi-tier computational…
Peak-Controlled Logits Poisoning Attack in Federated Distillation
Yuhan Tang, Aoxu Zhang, Zhiyuan Wu +4
Federated Distillation (FD) offers an innovative approach to distributed machine learning, leveraging knowledge distillation for efficient and flexible cross-device knowledge trans…
ZoFia: Zero-Shot Fake News Detection with Entity-Guided Retrieval and Multi-LLM Interaction
Lvhua Wu, Xuefeng Jiang, Sheng Sun +4
The rapid spread of fake news threatens social stability and public trust, highlighting the urgent need for its effective detection. Although large language models (LLMs) show pote…
FedICT: Federated Multi-task Distillation for Multi-access Edge Computing
Zhiyuan Wu, Sheng Sun, Yuwei Wang +4
The growing interest in intelligent services and privacy protection for mobile devices has given rise to the widespread application of federated learning in Multi-access Edge Compu…
from Benign import Toxic: Jailbreaking the Language Model via Adversarial Metaphors
Yu Yan, Sheng Sun, Zenghao Duan +5
Current studies have exposed the risk of Large Language Models (LLMs) generating harmful content by jailbreak attacks. However, they overlook that the direct generation of harmful…
Na'vi or Knave: Jailbreaking Language Models via Metaphorical Avatars
Yu Yan, Sheng Sun, Junqi Tong +2
Metaphor serves as an implicit approach to convey information, while enabling the generalized comprehension of complex subjects. However, metaphor can potentially be exploited to b…
REFOL: Resource-Efficient Federated Online Learning for Traffic Flow Forecasting
Qingxiang Liu, Sheng Sun, Yuxuan Liang +6
Multiple federated learning (FL) methods are proposed for traffic flow forecasting (TFF) to avoid heavy-transmission and privacy-leaking concerns resulting from the disclosure of r…
FedTrip: A Resource-Efficient Federated Learning Method with Triplet Regularization
Xujing Li, Min Liu, Sheng Sun +3
In the federated learning scenario, geographically distributed clients collaboratively train a global model. Data heterogeneity among clients significantly results in inconsistent…
FedCache: A Knowledge Cache-driven Federated Learning Architecture for Personalized Edge Intelligence
Zhiyuan Wu, Sheng Sun, Yuwei Wang +6
Edge Intelligence (EI) allows Artificial Intelligence (AI) applications to run at the edge, where data analysis and decision-making can be performed in real-time and close to data…
SVAFD: A Secure and Verifiable Co-Aggregation Protocol for Federated Distillation
Tian Wen, Sheng Sun, Yuwei Wang +4
Secure Aggregation (SA) is an indispensable component of Federated Learning (FL) that concentrates on privacy preservation while allowing for robust aggregation. However, most SA d…
Beyond Model Scale Limits: End-Edge-Cloud Federated Learning with Self-Rectified Knowledge Agglomeration
Zhiyuan Wu, Sheng Sun, Yuwei Wang +5
The rise of End-Edge-Cloud Collaboration (EECC) offers a promising paradigm for Artificial Intelligence (AI) model training across end devices, edge servers, and cloud data centers…
Spin and Orbital Angular Momenta of Electromagnetic Waves: From Classical to Quantum Forms
Wei E. I. Sha, Zhihao Lan, Menglin L. N. Chen +2
Angular momenta of electromagnetic waves are important both in concepts and applications. In this work, we systematically discuss two types of angular momenta, i.e., spin angular m…
Federated Skewed Label Learning with Logits Fusion
Yuwei Wang, Runhan Li, Hao Tan +5
Federated learning (FL) aims to collaboratively train a shared model across multiple clients without transmitting their local data. Data heterogeneity is a critical challenge in re…
Investigating Large Language Models for Code Vulnerability Detection: An Experimental Study
Xuefeng Jiang, Lvhua Wu, Sheng Sun +5
Code vulnerability detection (CVD) is essential for addressing and preventing system security issues, playing a crucial role in ensuring software security. Previous learning-based…
Online Spatio-Temporal Correlation-Based Federated Learning for Traffic Flow Forecasting
Qingxiang Liu, Sheng Sun, Min Liu +2
Traffic flow forecasting (TFF) is of great importance to the construction of Intelligent Transportation Systems (ITS). To mitigate communication burden and tackle with the problem…
EC2MoE: Adaptive End-Cloud Pipeline Collaboration Enabling Scalable Mixture-of-Experts Inference
Zheming Yang, Yunqing Hu, Sheng Sun +1
The Mixture-of-Experts (MoE) paradigm has emerged as a promising solution to scale up model capacity while maintaining inference efficiency. However, deploying MoE models across he…