1 citations · 1 across the 8 of their papers we have counts for
10 papers
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…
Sharing is Caring: Analysis of Hybrid Network Sharing Strategies for Energy Efficient Multi-Operator Cellular Systems
Laura Finarelli, Maoquan Ni, Michela Meo +2
This paper introduces a novel analytical framework for evaluating energy-efficient, QoS-aware network-sharing strategies in cellular networks. Leveraging stochastic geometry, our f…
Assessing the Benefits of Ground Vehicles as Moving Urban Base Stations
Laura Finarelli, Falko Dressler, Marco Ajmone Marsan +1
In the evolution towards 6G user-centric networking, the moving network (MN) paradigm can play an important role. In a MN, some small cell base stations (BS) are installed on top o…
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…