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Electronics
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Recent Advances in Blockchain Technology and Distributed AI Applications
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Recent Advances in Blockchain Technology and Distributed AI Applications

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (15 September 2023) | Viewed by 10324

Special Issue Editor

Dr. Imran

E-Mail Website
Guest Editor
Department of Biomedical Engineering, Gachon University, Incheon 21936, Republic of Korea
Interests: IoT; AI; blockchain; interdisciplinary research; IT convergence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

Distributed artificial intelligence (AI), also referred to as decentralized artificial intelligence, is a subfield of AI dedicated to developing distributed problem-solving solutions. Distributed AI is closely related to, and is a precursor to, the field of multi-agent systems. In recent research, AI (including advanced machine learning) has been widely adopted in blockchain-leveraged environments. Therefore, this Special Issue welcomes articles based on advances in blockchain technology and distributed AI applications. Moreover, we expect high-quality research in personalized and enterprise-level services management applications of distributed AI and blockchain. This Special Issue aims to invite research that covers recent trends in blockchain and distributed AI technologies. Furthermore, we expect articles related to topics of this Special Issue that will positively impact the scientific applications and add sustainability to improve and solve complex problems.

This Special Issue is open to multidisciplinary research on the convergence of blockchain and distributed AI for sustainable e-services and applications. It covers original research articles, reviews, and communication surveys in the described domain that include but are not limited to the following topics:

  • Federated learning-based applications.
  • Sustainable management of E-services.
  • Convergence applications of blockchain and distributed AI.
  • Smart spaces and distributed AI.
  • Big data intelligence and multiagent systems.
  • Distributed AI for trust and privacy issues.
  • Distributed AI in medical imaging.
  • Mathematical optimization in distributed AI.
  • Research trends in federated learning and Industry 5.0.

Dr. Imran
Guest Editor

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. Electronics is an international peer-reviewed open access semimonthly 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 2400 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

  • artificial intelligence 
  • distributed AI 
  • federated learning for sustainability 
  • blockchain 
  • e-healthcare services and blockchain 
  • smart spaces 
  • medical images 
  • mathematical optimization 
  • Industry 5.0 
  • multiagent systems

Published Papers (7 papers)

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Research

24 pages, 6709 KiB  
Article
A Federated Learning Method Based on Blockchain and Cluster Training
by Yue Li, Yiting Yan, Zengjin Liu, Chang Yin, Jiale Zhang and Zhaohui Zhang
Electronics 2023, 12(19), 4014; https://doi.org/10.3390/electronics12194014 - 23 Sep 2023
Cited by 1 | Viewed by 881
Abstract
Federated learning (FL) is an emerging machine learning method in which all participants can collaboratively train a model without sharing their raw data, thereby breaking down data silos and avoiding privacy issues caused by centralized data storage. In practical applications, client data are [...] Read more.
Federated learning (FL) is an emerging machine learning method in which all participants can collaboratively train a model without sharing their raw data, thereby breaking down data silos and avoiding privacy issues caused by centralized data storage. In practical applications, client data are non-independent and identically distributed, resulting in FL requiring multiple rounds of communication to converge, which entails high communication costs. Moreover, the centralized architecture of traditional FL remains susceptible to privacy breaches, network congestion, and single-point failures. In order to solve these problems, this paper proposes an FL framework based on blockchain technology and a cluster training algorithm, called BCFL. We first improved an FL algorithm based on odd–even round cluster training, which accelerates model convergence by dividing clients into clusters and adopting serialized training within each cluster. Meanwhile, compression operations were applied to model parameters before transmission to reduce communication costs and improve communication efficiency. Then, a decentralized FL architecture was designed and developed based on blockchain and Inter-Planetary File System (IPFS), where the blockchain records the FL process and IPFS optimizes the high storage costs associated with the blockchain. The experimental results demonstrate the superiority of the framework in terms of accuracy and communication efficiency. Full article
(This article belongs to the Special Issue Recent Advances in Blockchain Technology and Distributed AI Applications)
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16 pages, 1305 KiB  
Article
Adaptive Quantization Mechanism for Federated Learning Models Based on DAG Blockchain
by Tong Li, Chao Yang, Lei Wang, Tingting Li, Hai Zhao and Jiewei Chen
Electronics 2023, 12(17), 3712; https://doi.org/10.3390/electronics12173712 - 02 Sep 2023
Viewed by 880
Abstract
With the development of the power internet of things, the traditional centralized computing pattern has been difficult to apply to many power business scenarios, including power load forecasting, substation defect detection, and demand-side response. How to perform efficient and reliable machine learning tasks [...] Read more.
With the development of the power internet of things, the traditional centralized computing pattern has been difficult to apply to many power business scenarios, including power load forecasting, substation defect detection, and demand-side response. How to perform efficient and reliable machine learning tasks while ensuring that user data privacy is not violated has attracted the attention of the industry. Blockchain-based federated learning (FL), proposed as a new decentralized and distributed learning framework for building privacy-enhanced IoT systems, is receiving more and more attention from scholars. The framework provides some advantages, including decentralization, scalability, and data privacy, but at the same time its consensus mechanism consumes a significant amount of computational resources. Moreover, the number of model parameters has increased dramatically, leading to an increasing amount of transmitted data and a vast communication overhead. To reduce the communication overhead, we propose an FL framework in the directed acyclic graph (DAG)-based blockchain system, which achieves efficient and trusted sharing of FL networks. We design an adaptive model compression method based on k-means to compress the FL model and reduce the communication overhead of each round in FL training. Meanwhile, the original accuracy-based tips selection algorithm is optimized, and a tips selection algorithm considering multi-factor evaluation is proposed. Simulation experimental results show that the method proposed in this paper reduces the total bytes of communication of the blockchain-based federated learning system while balancing the accuracy of the FL model compared to previous work. Full article
(This article belongs to the Special Issue Recent Advances in Blockchain Technology and Distributed AI Applications)
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19 pages, 2782 KiB  
Article
Computation and Communication Efficient Adaptive Federated Optimization of Federated Learning for Internet of Things
by Zunming Chen, Hongyan Cui, Ensen Wu and Xi Yu
Electronics 2023, 12(16), 3451; https://doi.org/10.3390/electronics12163451 - 15 Aug 2023
Cited by 2 | Viewed by 936
Abstract
The proliferation of the Internet of Things (IoT) and widespread use of devices with sensing, computing, and communication capabilities have motivated intelligent applications empowered by artificial intelligence. Classical artificial intelligence algorithms require centralized data collection and processing, which are challenging in realistic intelligent [...] Read more.
The proliferation of the Internet of Things (IoT) and widespread use of devices with sensing, computing, and communication capabilities have motivated intelligent applications empowered by artificial intelligence. Classical artificial intelligence algorithms require centralized data collection and processing, which are challenging in realistic intelligent IoT applications due to growing data privacy concerns and distributed datasets. Federated Learning (FL) has emerged as a privacy-preserving distributed learning framework, which enables IoT devices to train global models through sharing model parameters. However, inefficiency due to frequent parameter transmissions significantly reduces FL performance. Existing acceleration algorithms consist of two main types including local update and parameter compression, which considers the trade-offs between communication and computation/precision, respectively. Jointly considering these two trade-offs and adaptively balancing their impacts on convergence have remained unresolved. To solve the problem, this paper proposes a novel efficient adaptive federated optimization (FedEAFO) algorithm to improve the efficiency of FL, which minimizes the learning error via jointly considering two variables including local update and parameter compression. The FedEAFO enables FL to adaptively adjust two variables and balance trade-offs among computation, communication, and precision. The experiment results illustrate that compared with state-of-the-art algorithms, the FedEAFO can achieve higher accuracies faster. Full article
(This article belongs to the Special Issue Recent Advances in Blockchain Technology and Distributed AI Applications)
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17 pages, 2361 KiB  
Article
MFLCES: Multi-Level Federated Edge Learning Algorithm Based on Client and Edge Server Selection
by Zhenpeng Liu, Sichen Duan, Shuo Wang, Yi Liu and Xiaofei Li
Electronics 2023, 12(12), 2689; https://doi.org/10.3390/electronics12122689 - 15 Jun 2023
Viewed by 867
Abstract
This research suggests a multi-level federated edge learning algorithm by leveraging the advantages of Edge Computing Paradigm. Model aggregation is partially moved from a cloud center server to edge servers in this framework, and edge servers are connected hierarchically depending on where they [...] Read more.
This research suggests a multi-level federated edge learning algorithm by leveraging the advantages of Edge Computing Paradigm. Model aggregation is partially moved from a cloud center server to edge servers in this framework, and edge servers are connected hierarchically depending on where they are located and how much computational power they have. At the same time, we considered an important issue: the heterogeneity of different client computing resources (such as device processor computing power) and server communication channels (which may be limited by geography or device). For this situation, a client and edge server selection algorithm (CESA) based on a greedy algorithm is proposed in this paper. Given resource constraints, CESA aims to select as many clients and edge servers as possible to participate in the model computation in order to improve the accuracy of the model. The simulation results show that, when the number of clients is high, the multi-level federated edge learning algorithm can shorten the model training time and improve efficiency compared to the traditional federated learning algorithm. Meanwhile, the CESA is able to aggregate more clients for training in the same amount of time compared to the baseline algorithm, improving model training accuracy. Full article
(This article belongs to the Special Issue Recent Advances in Blockchain Technology and Distributed AI Applications)
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38 pages, 3199 KiB  
Article
Blockchain-Based Trusted Federated Learning with Pre-Trained Models for COVID-19 Detection
by Genqing Bian, Wenjing Qu and Bilin Shao
Electronics 2023, 12(9), 2068; https://doi.org/10.3390/electronics12092068 - 30 Apr 2023
Cited by 3 | Viewed by 2454
Abstract
COVID-19 is a serious epidemic that not only endangers human health, but also wreaks havoc on the development of society. Recently, there has been research on using artificial intelligence (AI) techniques for COVID-19 detection. As AI has entered the era of big models, [...] Read more.
COVID-19 is a serious epidemic that not only endangers human health, but also wreaks havoc on the development of society. Recently, there has been research on using artificial intelligence (AI) techniques for COVID-19 detection. As AI has entered the era of big models, deep learning methods based on pre-trained models (PTMs) have become a focus of industrial applications. Federated learning (FL) enables the union of geographically isolated data, which can address the demands of big data for PTMs. However, the incompleteness of the healthcare system and the untrusted distribution of medical data make FL participants unreliable, and medical data also has strong privacy protection requirements. Our research aims to improve training efficiency and global model accuracy using PTMs for training in FL, reducing computation and communication. Meanwhile, we provide a secure aggregation rule using differential privacy and fully homomorphic encryption to achieve a privacy-preserving Byzantine robust federal learning scheme. In addition, we use blockchain to record the training process and we integrate a Byzantine fault tolerance consensus to further improve robustness. Finally, we conduct experiments on a publicly available dataset, and the experimental results show that our scheme is effective with privacy-preserving and robustness. The final trained models achieve better performance on the positive prediction and severe prediction tasks, with an accuracy of 85.00% and 85.06%, respectively. Thus, this indicates that our study is able to provide reliable results for COVID-19 detection. Full article
(This article belongs to the Special Issue Recent Advances in Blockchain Technology and Distributed AI Applications)
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14 pages, 2571 KiB  
Article
FedUKD: Federated UNet Model with Knowledge Distillation for Land Use Classification from Satellite and Street Views
by Renuga Kanagavelu, Kinshuk Dua, Pratik Garai, Neha Thomas, Simon Elias, Susan Elias, Qingsong Wei, Liu Yong and Goh Siow Mong Rick
Electronics 2023, 12(4), 896; https://doi.org/10.3390/electronics12040896 - 09 Feb 2023
Cited by 1 | Viewed by 2122
Abstract
Federated deep learning frameworks can be used strategically to monitor land use locally and infer environmental impacts globally. Distributed data from across the world would be needed to build a global model for land use classification. The need for a federated approach in [...] Read more.
Federated deep learning frameworks can be used strategically to monitor land use locally and infer environmental impacts globally. Distributed data from across the world would be needed to build a global model for land use classification. The need for a federated approach in this application domain would be to avoid the transfer of data from distributed locations and save network bandwidth to reduce communication costs. We used a federated UNet model for the semantic segmentation of satellite and street view images. The novelty of the proposed architecture involves the integration of knowledge distillation to reduce communication costs and response times. The accuracy obtained was above 95% and we also brought in a significant model compression to over 17 times and 62 times for street-view and satellite images, respectively. Our proposed framework has the potential to significantly improve the efficiency and privacy of real-time tracking of climate change across the planet. Full article
(This article belongs to the Special Issue Recent Advances in Blockchain Technology and Distributed AI Applications)
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20 pages, 4032 KiB  
Article
A Secure Storage and Deletion Verification Scheme of Microgrid Data Based on Integrating Blockchain into Edge Computing
by Lihua Zhang, Chunhui Liu, Boping Li, Haodong Fang and Jinguang Gu
Electronics 2022, 11(23), 4033; https://doi.org/10.3390/electronics11234033 - 05 Dec 2022
Viewed by 1155
Abstract
A microgrid generates a large amount of power data during daily operation, which needs to be safely transferred, stored, and deleted. In this paper, we propose a secure storage and deletion verification scheme that combines blockchain and edge computing for the problems of [...] Read more.
A microgrid generates a large amount of power data during daily operation, which needs to be safely transferred, stored, and deleted. In this paper, we propose a secure storage and deletion verification scheme that combines blockchain and edge computing for the problems of limited storage capacity of blockchain and unverifiable data deletion. Firstly, edge computing is used to preprocess power data to reduce the amount of data and to improve the quality of data. Secondly, a hybrid encryption method that combines the improved ElGamal algorithm and the AES-256 algorithm is used to encrypt outsourcing data, and a secure storage chain is built based on the K-Raft consensus protocol to ensure the security of data in the transmission process. Finally, after initiating a data deletion request and successfully deleting the data, a deletion proof is generated and stored in the chain built, based on the Streamlet consensus protocol. The experimental results illustrate that the basic computing cost, block generation time, and communication delay of this scheme are the most efficient; the efficiency of the improved ElGamal algorithm is three times that of the traditional algorithm; the transaction throughput of the the double-layer blockchain can reach 13,000 tps at most. This scheme can realize the safe storage of microgrid data, and can also realize the efficient deletion and verification of outsourcing data. Full article
(This article belongs to the Special Issue Recent Advances in Blockchain Technology and Distributed AI Applications)
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