Privacy-Preserving Data-Driven Learning Models for Emerging Communication Networks: A Comprehensive Survey
With the proliferation of Beyond 5G (B5G) communication systems and heterogeneous
networks, mobile broadband users are generating massive volumes of data that undergo …
networks, mobile broadband users are generating massive volumes of data that undergo …
[HTML][HTML] A survey on membership inference attacks and defenses in Machine Learning
Membership inference (MI) attacks mainly aim to infer whether a data record was used to
train a target model or not. Due to the serious privacy risks, MI attacks have been attracting a …
train a target model or not. Due to the serious privacy risks, MI attacks have been attracting a …
PPFed: A Privacy-Preserving and Personalized Federated Learning Framework
Federated learning is a distributed learning paradigm where a global model is trained using
data samples from multiple clients but without the necessity of sharing raw data samples …
data samples from multiple clients but without the necessity of sharing raw data samples …
Privacy-preserving collaborative intrusion detection in edge of internet of things: A robust and efficient deep generative learning approach
W Yao, H Zhao, H Shi - IEEE Internet of Things Journal, 2023 - ieeexplore.ieee.org
The swift expansion of the Internet of Things (IoT) has brought about convenient services,
but it has also increased cyber threats. An intrusion detection system (IDS) is an effective …
but it has also increased cyber threats. An intrusion detection system (IDS) is an effective …
Comparative analysis of membership inference attacks in federated learning
S Dayal, D Alhadidi, A Abbasi Tadi… - Proceedings of the 27th …, 2023 - dl.acm.org
Given a federated learning model and a record, a membership inference attack can
determine whether this record is part of the model's training dataset. Federated learning is a …
determine whether this record is part of the model's training dataset. Federated learning is a …
Advances and open challenges in federated learning with foundation models
The integration of Foundation Models (FMs) with Federated Learning (FL) presents a
transformative paradigm in Artificial Intelligence (AI), offering enhanced capabilities while …
transformative paradigm in Artificial Intelligence (AI), offering enhanced capabilities while …
Tokenization Matters! Degrading Large Language Models through Challenging Their Tokenization
Large Language Models (LLMs) have shown remarkable capabilities in language
understanding and generation. Nonetheless, it was also witnessed that LLMs tend to …
understanding and generation. Nonetheless, it was also witnessed that LLMs tend to …
Distributed computing in multi-agent systems: a survey of decentralized machine learning approaches
At present, there is a pressing need for data scientists and academic researchers to devise
advanced machine learning and artificial intelligence-driven systems that can effectively …
advanced machine learning and artificial intelligence-driven systems that can effectively …
Secure Decentralized Aggregation to Prevent Membership Privacy Leakage in Edge-based Federated Learning
M Shen, J Wang, J Zhang, Q Zhao… - … on Network Science …, 2024 - ieeexplore.ieee.org
Federated Learning (FL) is a machine learning approach that enables multiple users to
share their local models for the aggregation of a global model, protecting data privacy by …
share their local models for the aggregation of a global model, protecting data privacy by …
Efficient Membership Inference Attacks against Federated Learning via Bias Differences
L Zhang, L Li, X Li, B Cai, Y Gao, R Dou… - Proceedings of the 26th …, 2023 - dl.acm.org
Federated learning aims to complete model training without private data sharing, but many
privacy risks remain. Recent studies have shown that federated learning is vulnerable to …
privacy risks remain. Recent studies have shown that federated learning is vulnerable to …