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

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: 20 May 2025 | Viewed by 3828

Special Issue Editors

Prof. Dr. Qiang Duan

E-Mail Website
Guest Editor
Information Sciences and Technology Department, Pennsylvania State University, Abington, PA 19001, USA
Interests: network virtualization; cloud-native networking; edge-cloud computing; federated-split learning; internet of things; internet of intelligence
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Zhihui Lu

E-Mail Website
Guest Editor
School of Computer Science, Fudan University, Shanghai 200433, China
Interests: edge-cloud computing; service computing; big data architecture; internet of things; distributed systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Future Internet is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

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

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Related Special Issue

Published Papers (4 papers)

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Research

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38 pages, 4044 KiB  
Article
Trustworthy AI and Federated Learning for Intrusion Detection in 6G-Connected Smart Buildings
by Rosario G. Garroppo, Pietro Giuseppe Giardina, Giada Landi and Marco Ruta
Future Internet 2025, 17(5), 191; https://doi.org/10.3390/fi17050191 - 23 Apr 2025
Viewed by 159
Abstract
Smart building applications require robust security measures to ensure system functionality, privacy, and security. To this end, this paper proposes a Federated Learning Intrusion Detection System (FL-IDS) composed of two convolutional neural network (CNN) models to detect network and IoT device attacks simultaneously. [...] Read more.
Smart building applications require robust security measures to ensure system functionality, privacy, and security. To this end, this paper proposes a Federated Learning Intrusion Detection System (FL-IDS) composed of two convolutional neural network (CNN) models to detect network and IoT device attacks simultaneously. Collaborative training across multiple cooperative smart buildings enables model development without direct data sharing, ensuring privacy by design. Furthermore, the design of the proposed method considers three key principles: sustainability, adaptability, and trustworthiness. The proposed data pre-processing and engineering system significantly reduces the amount of data to be processed by the CNN, helping to limit the processing load and associated energy consumption towards more sustainable Artificial Intelligence (AI) techniques. Furthermore, the data engineering process, which includes sampling, feature extraction, and transformation of data into images, is designed considering its adaptability to integrate new sensor data and to fit seamlessly into a zero-touch system, following the principles of Machine Learning Operations (MLOps). The designed CNNs allow for the investigation of AI reasoning, implementing eXplainable AI (XAI) techniques such as the correlation map analyzed in this paper. Using the ToN-IoT dataset, the results show that the proposed FL-IDS achieves performance comparable to that of its centralized counterpart. To address the specific vulnerabilities of FL, a secure and robust aggregation method is introduced, making the system resistant to poisoning attacks from up to 20% of the participating clients. Full article
(This article belongs to the Special Issue Edge-Cloud Computing and Federated-Split Learning in Internet of Things—Second Edition)
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20 pages, 833 KiB  
Article
Mobility Prediction and Resource-Aware Client Selection for Federated Learning in IoT
by Rana Albelaihi
Future Internet 2025, 17(3), 109; https://doi.org/10.3390/fi17030109 - 1 Mar 2025
Viewed by 537
Abstract
This paper presents the Mobility-Aware Client Selection (MACS) strategy, developed to address the challenges associated with client mobility in Federated Learning (FL). FL enables decentralized machine learning by allowing collaborative model training without sharing raw data, preserving privacy. However, client mobility and limited [...] Read more.
This paper presents the Mobility-Aware Client Selection (MACS) strategy, developed to address the challenges associated with client mobility in Federated Learning (FL). FL enables decentralized machine learning by allowing collaborative model training without sharing raw data, preserving privacy. However, client mobility and limited resources in IoT environments pose significant challenges to the efficiency and reliability of FL. MACS is designed to maximize client participation while ensuring timely updates under computational and communication constraints. The proposed approach incorporates a Mobility Prediction Model to forecast client connectivity and resource availability and a Resource-Aware Client Evaluation mechanism to assess eligibility based on predicted latencies. MACS optimizes client selection, improves convergence rates, and enhances overall system performance by employing these predictive capabilities and a dynamic resource allocation strategy. The evaluation includes comparisons with advanced baselines such as Reinforcement Learning-based FL (RL-based) and Deep Learning-based FL (DL-based), in addition to Static and Random selection methods. For the CIFAR dataset, MACS achieved a final accuracy of 95%, outperforming Static selection (85%), Random selection (80%), RL-based FL (90%), and DL-based FL (93%). Similarly, for the MNIST dataset, MACS reached 98% accuracy, surpassing Static selection (92%), Random selection (88%), RL-based FL (94%), and DL-based FL (96%). Additionally, MACS consistently required fewer iterations to achieve target accuracy levels, demonstrating its efficiency in dynamic IoT environments. This strategy provides a scalable and adaptable solution for sustainable federated learning across diverse IoT applications, including smart cities, healthcare, and industrial automation. Full article
(This article belongs to the Special Issue Edge-Cloud Computing and Federated-Split Learning in Internet of Things—Second Edition)
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24 pages, 3664 KiB  
Article
FI-NL2PY2SQL: Financial Industry NL2SQL Innovation Model Based on Python and Large Language Model
by Xiaozheng Du, Shijing Hu, Feng Zhou, Cheng Wang and Binh Minh Nguyen
Future Internet 2025, 17(1), 12; https://doi.org/10.3390/fi17010012 - 2 Jan 2025
Cited by 1 | Viewed by 2018
Abstract
With the rapid development of prominent models, NL2SQL has made many breakthroughs, but customers still hope that the accuracy of NL2SQL can be continuously improved through optimization. The method based on large models has brought revolutionary changes to NL2SQL. This paper innovatively proposes [...] Read more.
With the rapid development of prominent models, NL2SQL has made many breakthroughs, but customers still hope that the accuracy of NL2SQL can be continuously improved through optimization. The method based on large models has brought revolutionary changes to NL2SQL. This paper innovatively proposes a new NL2SQL method based on a large language model (LLM), which could be adapted to an edge-cloud computing platform. First, natural language is converted into Python language, and then SQL is generated through Python. At the same time, considering the traceability characteristics of financial industry regulatory requirements, this paper uses the open-source big model DeepSeek. After testing on the BIRD dataset, compared with most NL2SQL models based on large language models, EX is at least 2.73% higher than the original method, F1 is at least 3.72 higher than the original method, and VES is 6.34% higher than the original method. Through this innovative algorithm, the accuracy of NL2SQL in the financial industry is greatly improved, which can provide business personnel with a robust database access mode. Full article
(This article belongs to the Special Issue Edge-Cloud Computing and Federated-Split Learning in Internet of Things—Second Edition)
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Review

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24 pages, 2225 KiB  
Review
A Review and Experimental Evaluation on Split Learning
by Zhanyi Hu, Tianchen Zhou, Bingzhe Wu, Cen Chen and Yanhao Wang
Future Internet 2025, 17(2), 87; https://doi.org/10.3390/fi17020087 - 13 Feb 2025
Viewed by 1024
Abstract
Training deep learning models collaboratively on decentralized edge devices has attracted significant attention recently. The two most prominent schemes for this problem are Federated Learning (FL) and Split Learning (SL). Although there have been several surveys and experimental evaluations for FL in the [...] Read more.
Training deep learning models collaboratively on decentralized edge devices has attracted significant attention recently. The two most prominent schemes for this problem are Federated Learning (FL) and Split Learning (SL). Although there have been several surveys and experimental evaluations for FL in the literature, SL paradigms have not yet been systematically reviewed and evaluated. Due to the diversity of SL paradigms in terms of label sharing, model aggregation, cut layer selection, etc., the lack of a systematic survey makes it difficult to fairly and conveniently compare the performance of different SL paradigms. To address the above issue, in this paper, we first provide a comprehensive review for existing SL paradigms. Then, we implement several typical SL paradigms and perform extensive experiments to compare their performance in different scenarios on four widely used datasets. The experimental results provide extensive engineering advice and research insights for SL paradigms. We hope that our work can facilitate future research on SL by establishing a fair and accessible benchmark for SL performance evaluation. Full article
(This article belongs to the Special Issue Edge-Cloud Computing and Federated-Split Learning in Internet of Things—Second Edition)
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