Secure Federated Learning for Cognitive Radio Sensing

Article Telecommunications

Federated Learning-Based Cooperative Spectrum Sensing in Cognitive Radio

Zhibo Chen et al.

Summary: The study introduces a federated learning-based spectrum sensing algorithm, utilizing a distributed learning framework where each secondary user can train with local data, ensuring data privacy and significantly reducing transmission load.

IEEE COMMUNICATIONS LETTERS (2022)

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Article Computer Science, Information Systems

Recent Advances on Federated Learning for Cybersecurity and Cybersecurity for Federated Learning for Internet of Things

Bimal Ghimire et al.

Summary: This article discusses the decentralized paradigm in the field of cybersecurity and machine learning for the emerging Internet of Things (IoT). It highlights the concept of federated cybersecurity (FC) and the application of federated learning (FL) in securing the IoT environment. The article also explores the performance issues and future research trends in this area.

IEEE INTERNET OF THINGS JOURNAL (2022)

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Article

Security of Federated Learning: Attacks, Defensive Mechanisms, and Challenges

Mourad Benmalek et al.

Revue d'Intelligence Artificielle (2022)

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Article Engineering, Electrical & Electronic

When Machine Learning Meets Spectrum Sharing Security: Methodologies and Challenges

Qun Wang et al.

Summary: This article provides a comprehensive survey of the recent development of machine learning-based spectrum sharing (SS) methods and the corresponding defense mechanisms for security issues. Various ML-based approaches for improving the performance of SS communication systems are discussed in detail, along with security challenges and open issues in this field.

IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY (2022)

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Article Engineering, Electrical & Electronic

Distributed Learning in Wireless Networks: Recent Progress and Future Challenges

Mingzhe Chen et al.

Summary: This paper provides a comprehensive study on efficiently deploying distributed learning over wireless edge networks, presenting several emerging paradigms including federated learning, federated distillation, distributed inference, and multi-agent reinforcement learning. It addresses challenges such as uncertain wireless environment, limited resources, and hardware constraints, offering guidelines and future research opportunities.

IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS (2021)

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Article Engineering, Electrical & Electronic