Edge-Cloud Computing and Federated-Split Learning in Internet of Things—Second Edition

Dear Colleagues,

The wide deployment of the Internet of Things (IoT) calls for new machine learning (ML) methods and distributed computing paradigms to enable various ML-based IoT applications to effectively process the huge amount of data in IoT. Federated Learning (FL) is a new collaborative learning method that allows multiple data owners to cooperate in ML model training without exposing private data. Split Learning (SL) is an emerging collaborative learning method that splits an ML model into multiple portions that are trained collaboratively by different entities. FL and SL, each have unique advantages and respective limitations, may complement each other to facilitate effective collaborative learning in an IoT environment. At the same time, multimodal large models, especially large language model（LLM）technology, as a representative of the new generation of artificial intelligence technology, have also developed rapidly in recent years. On the other hand, the rapid development of edge-cloud computing technologies enables a distributed computing platform in IoT upon which the FL and SL frameworks can be deployed. Therefore, FL, SL, and multimodal Large Model upon an edge-cloud computing platform in an IoT environment have formed an active research area that attracts interest from both academia and industry.

This Special Issue aims to present the latest research advances in this interdisciplinary field of edge-cloud computing, federated-split learning, and multimodal large models. The Special Issue covers, but is not limited to, the following topics:

• Algorithms for federated learning in edge-cloud computing;
• Model aggregation for federated learning;
• Communication-efficient federated learning;
• Client incentive and selection in federated learning;
• Decentralized framework architecture of federated learning;
• Split learning algorithms and frameworks;
• Split learning upon an edge-cloud computing platform;
• Split learning performance evaluation and improvement;
• Large model training and inference upon an edge-cloud computing platform;
• Combining federated learning and split learning;
• Hybrid federated–split learning frameworks upon an edge-cloud computing platform;
• Privacy and security issues of federated and split learning;
• Applications of federated and split learning in IoT (e.g., in industrial IoT, smart city, smart transportation, and smart health environments);
• Blockchain-assisted federated and split learning;
• Resource management in edge-cloud computing for supporting federated and split learning;
• Unified computation-network virtualization in edge-cloud computing for supporting federated and split learning;
• Service orchestration in edge-cloud computing for supporting federated and split learning.

Prof. Dr. Qiang Duan
Prof. Dr. Zhihui Lu
Guest Editors

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• federated learning
• split learning
• hybrid federated–split learning
• edge-cloud computing
• internet of things
• multimodal large models
• large language model (LLM)

• Edge-Cloud Computing and Federated-Split Learning in the Internet of Things in Future Internet (12 articles)