On the Efficiency of Federated Learning for 6G-Enabled IoT and WSNs-Based Critical Infrastructures
A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Internet of Things".
Deadline for manuscript submissions: closed (20 July 2023) | Viewed by 391
Special Issue Editors
Interests: Internet of Things (IoTs); Wireless Sensor Networks (WSNs); Artificial Intelligent (AI)-based critical infrastructures; Security and Privacy of Digital Healthcare Infrastructures; Empirical measurements for Internet of Things (IoTs) Security; Privacy and Security in Network
Special Issues, Collections and Topics in MDPI journals
Interests: wireless communication and mobile networking sensor networks wireless; mobile Ad hoc and mesh networks QoS in heterogeneous wired and wireless networks security: Ad hoc, sensor and ubiquitous networks performance evaluation: markovian process, SAN, queuing networks cloud computing
Special Issue Information
Dear Colleagues,
Internet of Things (IoT) and Wireless Sensor Networks (WSNs) applications, especially critical infrastructure-based applications such as healthcare monitoring and intelligent transportation, are expected to release significant advances in our lives. The cellular networks, especially 6G, have become the major factor supporting IoT and WSNs services because of their incredibly high reliability, lower power consumption and lower latency. Federated Learning (FL) has recently gained popularity in IoT and WSNs applications as a distributed machine learning approach that preserves data privacy. As FL becomes increasingly reliable with low latency, more applications in 6G will benefit from it, allowing future 6G systems to provide high privacy and security. However, there is still a lot of work to be done in the direction of more reliable, low-latency, and efficient FL for 6G-enabled IoT and WSN critical infrastructures.
Potential topics include but are not limited to:
- Reliable FL for 6G-based IoT or WSNs.
- Efficient FL for 6G-based infrastructures.
- AI-based FL applications.
- Simulation and performance evaluation mechanisms for IoT networks.
- FL in critical Applications.
- Privacy-Preserving FL for IoT or WSN-based applications.
- Critical IoT applications benefit from distributed and federated learning.
- Privacy, Security and trust in IoT environments.
Dr. Safa Otoum
Prof. Dr. Jalel Ben-Othman
Guest Editors
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