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Guaranteeing Data Privacy in Federated Unlearning with Dynamic User Participation

Autor: Liu, Ziyao, Jiang, Yu, Jiang, Weifeng, Guo, Jiale, Zhao, Jun, Lam, Kwok-Yan

Federated Unlearning (FU) is gaining prominence for its capability to eliminate influences of Federated Learning (FL) users' data from trained global FL models. A straightforward FU method involves removing the unlearned users and subsequently retrai

Externí odkaz: http://arxiv.org/abs/2406.00966

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Privacy-Preserving Federated Unlearning with Certified Client Removal

Autor: Liu, Ziyao, Ye, Huanyi, Jiang, Yu, Shen, Jiyuan, Guo, Jiale, Tjuawinata, Ivan, Lam, Kwok-Yan

In recent years, Federated Unlearning (FU) has gained attention for addressing the removal of a client's influence from the global model in Federated Learning (FL) systems, thereby ensuring the ``right to be forgotten" (RTBF). State-of-the-art method

Externí odkaz: http://arxiv.org/abs/2404.09724

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Decentralized Multimedia Data Sharing in IoV: A Learning-based Equilibrium of Supply and Demand

Autor: Fan, Jiani, Xu, Minrui, Guo, Jiale, Shar, Lwin Khin, Kang, Jiawen, Niyato, Dusit, Lam, Kwok-Yan

Publikováno v: IEEE Transactions on Vehicular Technology (Volume: 73, Issue: 3, March 2024)

The Internet of Vehicles (IoV) has great potential to transform transportation systems by enhancing road safety, reducing traffic congestion, and improving user experience through onboard infotainment applications. Decentralized data sharing can impr

Externí odkaz: http://arxiv.org/abs/2403.20218

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Differentiated Security Architecture for Secure and Efficient Infotainment Data Communication in IoV Networks

Autor: Fan, Jiani, Shar, Lwin Khin, Guo, Jiale, Yang, Wenzhuo, Niyato, Dusit, Lam, Kwok-Yan

This paper aims to provide differentiated security protection for infotainment data communication in Internet-of-Vehicle (IoV) networks. The IoV is a network of vehicles that uses various sensors, software, built-in hardware, and communication techno

Externí odkaz: http://arxiv.org/abs/2403.20136

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Efficient Dropout-resilient Aggregation for Privacy-preserving Machine Learning

Autor: Liu, Ziyao, Guo, Jiale, Lam, Kwok-Yan, Zhao, Jun

With the increasing adoption of data-hungry machine learning algorithms, personal data privacy has emerged as one of the key concerns that could hinder the success of digital transformation. As such, Privacy-Preserving Machine Learning (PPML) has rec

Externí odkaz: http://arxiv.org/abs/2203.17044

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Privacy-Preserving Aggregation in Federated Learning: A Survey

Autor: Liu, Ziyao, Guo, Jiale, Yang, Wenzhuo, Fan, Jiani, Lam, Kwok-Yan, Zhao, Jun

Over the recent years, with the increasing adoption of Federated Learning (FL) algorithms and growing concerns over personal data privacy, Privacy-Preserving Federated Learning (PPFL) has attracted tremendous attention from both academia and industry

Externí odkaz: http://arxiv.org/abs/2203.17005

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