Fedcomm: A privacy-enhanced and efficient authentication protocol for federated learning...

Security of federated learning in 6G era: A review on conceptual techniques and software platforms used for research and analysis

SHA Kazmi, F Qamar, R Hassan, K Nisar… - Computer Networks, 2024 - Elsevier
Federated Learning (FL) is an emerging Artificial Intelligence (AI) paradigm enabling
multiple parties to train a model collaboratively without sharing their data. With the upcoming …
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FedHGL: Cross-Institutional Federated Heterogeneous Graph Learning for IoT

X Wei, G Chen, Y Zhu, F Hu, C Zhang… - IEEE Internet of …, 2024 - ieeexplore.ieee.org
Graph neural networks, effectively harnessing the extensive interactive data from Internet of
Things (IoT) devices, significantly enhance service quality in IoT systems. However …
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Assessing Membership Leakages via Task-Aligned Divergent Shadow Datasets in Vehicular Road Cooperation

P Liu, W Wang, X Xu, H Li… - IEEE Internet of Things …, 2024 - ieeexplore.ieee.org
Deep classification models have been widely utilized in Vehicular Road Cooperation.
However, previous work indicates that deep classification models are vulnerable to the …
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[HTML] arxiv.org

Chu-ko-nu: A Reliable, Efficient, and Anonymously Authentication-Enabled Realization for Multi-Round Secure Aggregation in Federated Learning

K Cui, X Feng, L Wang, H Wu, X Zhang… - arXiv preprint arXiv …, 2024 - arxiv.org
Secure aggregation enables federated learning (FL) to perform collaborative training of
clients from local gradient updates without exposing raw data. However, existing secure …
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[PDF] acm.org

PnA: Robust Aggregation Against Poisoning Attacks to Federated Learning for Edge Intelligence

J Liu, X Lyu, L Duan, Y He, J Liu, H Ma, B Wang… - ACM Transactions on … - dl.acm.org
Federated learning (FL), which holds promise for use in edge intelligence applications for
smart cities, enables smart devices collaborate in training a global model by exchanging …
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