4 citations · 5 across the 11 of their papers we have counts for
12 papers
Byzantine-Resilient Federated Large Language Model via Hypergraph Message Passing
Kai Li, Hanlin Cai, Zihao Liu +5
Federated Large Language Models (FedLLMs) are promising to deliver privacy-preserving, collaborative intelligence. The decentralized training process is vulnerable to Byzantine att…
FedLNS: Leverage LayerNorm Signature Modeling to Mitigate Adversarial Manipulation in Federated LLMs
Kai Li, Jong-Ik Park, Carlee Joe-Wong +2
Federated training enables language models to learn from distributed private text, but the server cannot directly verify the local supervision or optimization process that produces…
When Agentic AI Meets Integrated Sensing and Communication
Kai Li, Conggai Li, Sarah Ali Siddiqui +4
Agentic artificial intelligence (AI) is transforming Integrated Sensing and Communication (ISAC) from a function-oriented physical-layer technology into a goal-driven, closed-loop…
Strategic Task Offloading for Delay-Sensitive IoT Applications: A Game-Theory-Based Demand-Supply Mechanism with Participation Incentives
Azadeh Pourkabirian, Amir Masoud Rahmani, Kai Li +1
Delay-sensitive Internet of Things (IoT) applications have drawn significant attention. Running many of these applications on IoT devices is challenging due to the limited processi…
A New Hybrid Precoding Approach for Multi-user Massive MIMO over Fading Channels
Azadeh Pourkabirian, Kai Li, Photios A. Stavrou +1
Hybrid precoding is an indispensable technique to harness the full potential of a multi-user massive multiple-input, multiple-output (MU-MMIMO) system. In this paper, we propose a…
Zero-Trust Foundation Models: A New Paradigm for Secure and Collaborative Artificial Intelligence for Internet of Things
Kai Li, Conggai Li, Xin Yuan +8
This paper focuses on Zero-Trust Foundation Models (ZTFMs), a novel paradigm that embeds zero-trust security principles into the lifecycle of foundation models (FMs) for Internet o…
Undermining Federated Learning Accuracy in EdgeIoT via Variational Graph Auto-Encoders
Kai Li, Shuyan Hu, Bochun Wu +3
EdgeIoT represents an approach that brings together mobile edge computing with Internet of Things (IoT) devices, allowing for data processing close to the data source. Sending sour…
Towards Resilient Federated Learning in CyberEdge Networks: Recent Advances and Future Trends
Kai Li, Zhengyang Zhang, Azadeh Pourkabirian +3
In this survey, we investigate the most recent techniques of resilient federated learning (ResFL) in CyberEdge networks, focusing on joint training with agglomerative deduction and…
A Novel Framework of Horizontal-Vertical Hybrid Federated Learning for EdgeIoT
Kai Li, Yilei Liang, Xin Yuan +4
This letter puts forth a new hybrid horizontal-vertical federated learning (HoVeFL) for mobile edge computing-enabled Internet of Things (EdgeIoT). In this framework, certain EdgeI…
Human-In-The-Loop Machine Learning for Safe and Ethical Autonomous Vehicles: Principles, Challenges, and Opportunities
Yousef Emami, Mohammadhossein Homaei, Miguel Gutiérrez Gaitán +4
Machine Learning (ML) has become central to Autonomous Vehicles (AVs), supporting perception, prediction, planning, control, and decision-making in dynamic environments. However, a…
A Novel Defense Against Poisoning Attacks on Federated Learning: LayerCAM Augmented with Autoencoder
Jingjing Zheng, Xin Yuan, Kai Li +3
Recent attacks on federated learning (FL) can introduce malicious model updates that circumvent widely adopted Euclidean distance-based detection methods. This paper proposes a nov…
Leverage Variational Graph Representation For Model Poisoning on Federated Learning
Kai Li, Xin Yuan, Jingjing Zheng +3
This paper puts forth a new training data-untethered model poisoning (MP) attack on federated learning (FL). The new MP attack extends an adversarial variational graph autoencoder…