Emerging Trends in Federated Learning and Network Security

Dear Colleagues,

In recent years, rapid advancements in artificial intelligence (AI) and machine learning (ML) have revolutionized numerous sectors, including manufacturing, healthcare, finance, and autonomous systems. These technological leaps have not only enhanced efficiency and productivity but have also paved the way for innovative solutions to complex problems. Among these advancements, federated learning (FL) has emerged as a groundbreaking approach that enables decentralized machine learning across distributed data sources while preserving data privacy. Unlike traditional centralized machine learning paradigms that require data to be aggregated in a single location, FL allows models to be trained on local devices where the data reside. This ensures that sensitive information remains localized, thus addressing critical privacy concerns and complying with data protection regulations.

This Special Issue, "Emerging Trends in Federated Learning and Network Security," aims to provide a comprehensive overview of the latest research, innovations, and applications of federated learning and network security. As federated learning continues to gain traction, understanding the associated security and networking challenges is paramount for its successful deployment and widespread adoption. FL relies heavily on decentralized networks to enable distributed machine learning across multiple devices or nodes, making the underlying communication infrastructure a crucial factor in ensuring both performance and security. In this context, network security, data privacy, and robust communication protocols are essential to safeguard sensitive data and maintain the integrity of distributed learning environments. This Special Issue will delve into various facets of FL and network security, including the implications of networking frameworks, secure communication channels, and how they influence the design and deployment of federated learning systems.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but not limited to) the following:

• Future directions and challenges in federated learning and network security;
• Network security and privacy concerns in federated learning;
• Transfer learning in federated learning;
• Novel algorithms for efficient federated learning;
• Applications of federated learning in industrial scenarios;
• Federated learning for smart grid data analytics;
• Blockchain and federated learning;
• Smart/intelligent manufacturing and Industry 4.0 based on federated learning;
• Federated learning for predictive maintenance in engineering systems;
• Federated learning network-based IDS and IPS;
• Distributed learning in distributed IDS;
• Federated learning-based anomaly detection for security attacks;
• Federated learning-based security mechanisms for edge computing;
• Securing federated learning communications;
• Adversarial attacks and defenses in federated learning networks;
• Federated learning for secure IoT networks;
• Trust management in federated learning networks.

Dr. Wei Guo
Dr. Maolin Yang
Dr. Qingzong Li
Prof. Dr. Pingyu Jiang
Guest Editors

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• machine learning
• federated learning
• network security
• data privacy preserving
• IoT
• smart/intelligent manufacturing
• smart grid
• intrusion detection and prevention