Distributed Learning for Privacy-Aware Cloud-Edge Processing in Health Sensor Networks

Dear Colleagues,

The convergence of intelligent health sensors and artificial intelligence is revolutionising medical monitoring, diagnostics, and personalised care. However, the centralised collection of sensitive data for model training raises significant concerns around privacy, bandwidth, and latency. Cloud platforms play a vital role in aggregating model updates, orchestrating learning across distributed nodes, and providing scalable infrastructure for real-time analytics and long-term storage of healthcare data. This Special Issue explores the critical shift towards decentralised and privacy-preserving AI paradigms that process data closer to its source.  

The primary aim of this Special Issue is to collate and disseminate high-quality, original research that addresses the key challenges and opportunities at the intersection of intelligent sensing, distributed artificial intelligence, and privacy-preserving computing in healthcare.

The scope of this Special Issue encompasses the entire technological stack required to realise effective federated and distributed learning for health sensing, from theoretical algorithms to real-world applications. It is centred on seamless and secure operation across the sensor–edge–cloud continuum. Specifically, the issue will cover:

• Theoretical and Algorithmic Innovations
  • Novel federated and distributed learning algorithms for health data;
  • Communication efficiency, non-identically distributed data handling;
  • Cloud-edge processing in health sensor networks (such as federated or collaborative learning over wearable, IoMT devices).
• Architectural and Systems Design
  • Scalable software and hardware architectures for edge-based learning;
  • Orchestration across heterogeneous devices and secure data pipelines.
• Security, Privacy, and Trust
  • Privacy-aware cloud-edge processing in health sensor networks;
  • Defence against ML-specific attacks and IoT/network threats.
• Application-Oriented Research
  • Real-world case studies and pilot deployments;
  • Benchmarking for remote monitoring, personalised care, and disease detection.

We welcome submissions in various formats, including original research articles, reviews, and case studies that address the unique challenges of applying Federated Learning (FL) and Distributed Machine Learning (DML) across the sensor–edge–cloud continuum. The focus is on novel architectures and efficient algorithms that enable collaborative learning from distributed health sensor networks—such as wearables, medical devices, and ambient sensors—without the need to exchange raw, private data. This issue aims to showcase cutting-edge solutions that ensure scalability, robustness, and security, while pushing the boundaries of what is possible in modern health informatics. This issue also addresses cloud-specific challenges such as secure data synchronisation, regulatory compliance in cloud environments, and efficient bandwidth utilisation for health data transmission.

Dr. Nadia Kanwal
Dr. Amro Al-Said Ahmad
Guest Editors

Manuscript Submission Information

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