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Federated and Distributed Learning in IoT
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Federated and Distributed Learning in IoT

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: 31 October 2024 | Viewed by 10605

Special Issue Editors

Dr. Mike Oluwatayo Ojo

E-Mail Website
Guest Editor
Texas A&M AgriLife Research, Texas A&M University, 17360 Coit Rd, Dallas, TX 75252, USA
Interests: Internet of Things; Industrial Internet of Things; edge AI; smart agriculture; optimization; LPWAN; software defined networks; network function virtualization; communication networks; 5G
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Stefano Giordano

E-Mail Website
Guest Editor
Dipartimento di Ingegneria della Informazione (DII), Università di Pisa, Via G. Caruso 16, 56122 Pisa, Italy
Interests: industrial IoT; agriculture; tactile internet; tele surgery
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Periklis Chatzimisios

E-Mail Website
Guest Editor
Department of Science and Technology, International Hellenic University, 570 01 Nea Moudania, Greece
Interests: IEEE 802.11 standards; Internet of Things; low-power protocols; smart cities
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The explosion of interest in the Internet of Things (IoT) has introduced new innovative services and applications in various sectors such as healthcare, agriculture, manufacturing, fleet management, etc., but also created new challenges due to its complexity, dynamicity, and heterogeneity. The development in the IoT has opened new avenues for artificial intelligence and machine learning. Towards this end, many research efforts have recently been undertaken to apply different machine learning techniques to the IoT to realize a greater efficiency.

Federated learning (FL) and distributed learning (DL) are emerging machine learning techniques with great potential in distributed applications, where data are typically generated and collected at the client-side while the collected data are processed by the application deployed at the server-side, thus addressing privacy and security concerns. However, there are still significant gaps and unresolved technical challenges in having systems that are scalable, robust, and able to handle exponentially growing data, as well as evaluating the performance of such techniques concerning their practicality in the IoT.

This Special Issue aims to bring together researchers and engineers in the field of IoT, machine learning, communications, and networking, in order to address the emerging challenges in the IoT, by seeking original, previously unpublished papers empirically addressing key issues and challenges related to the design, implementation, deployment, operation, optimization and evaluation of novel approaches based on the use of FL and DL solutions for the IoT. Potential topics include, but are not limited to:

  • FL and DL for IoT;
  • IoT network design and optimization;
  • Massive learning and computing methods, algorithms, and systems for the IoT;
  • Data-driven analysis and model on DL/FL and computing in the IoT;
  • DL in multi-agent systems;
  • Communication efficiency in FL;
  • FL and DL for large-scale Internet of Things;
  • FL and DL for future internet architectures;
  • FL and DL in the industrial Internet of Things;
  • Split learning and ensemble learning for IoT;
  • Low latency and highly reliable communications for collaborative computing in the IoT;
  • Trusted and collaborative framework for deep learning in the IoT;
  • Various applications supported by DL and FL and computing in IoT systems;
  • Applications of FL and DL in large-scale intelligent networking services;
  • FL and DL for next-generation networking (NGN) technologies and 6G;
  • Architectures, techniques and applications of intelligent edge cloud;
  • Security and privacy schemes for massive learning and computing in the IoT.

Dr. Mike Oluwatayo Ojo
Prof. Dr. Stefano Giordano
Prof. Dr. Periklis Chatzimisios
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. Sensors 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 2600 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

  • Internet of Things
  • distributed learning
  • federated learning
  • machine learning
  • intelligence systems
  • artificial intelligence
  • communication networks
  • edge intelligence
  • cognitive communication and networking
  • cloud computing/edge computing
  • big data analytics
  • privacy and security
  • industrial Internet of Things

Published Papers (6 papers)

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Research

18 pages, 910 KiB  
Article
Secure Aggregation Protocol Based on DC-Nets and Secret Sharing for Decentralized Federated Learning
by Diogo Pereira, Paulo Ricardo Reis and Fábio Borges
Sensors 2024, 24(4), 1299; https://doi.org/10.3390/s24041299 - 17 Feb 2024
Viewed by 627
Abstract
In the era of big data, millions and millions of data are generated every second by different types of devices. Training machine-learning models with these data has become increasingly common. However, the data used for training are often sensitive and may contain information [...] Read more.
In the era of big data, millions and millions of data are generated every second by different types of devices. Training machine-learning models with these data has become increasingly common. However, the data used for training are often sensitive and may contain information such as medical, banking, or consumer records, for example. These data can cause problems in people’s lives if they are leaked and also incur sanctions for companies that leak personal information for any reason. In this context, Federated Learning emerges as a solution to the privacy of personal data. However, even when only the gradients of the local models are shared with the central server, some attacks can reconstruct user data, allowing a malicious server to violate the FL principle, which is to ensure the privacy of local data. We propose a secure aggregation protocol for Decentralized Federated Learning, which does not require a central server to orchestrate the aggregation process. To achieve this, we combined a Multi-Secret-Sharing scheme with a Dining Cryptographers Network. We validate the proposed protocol in simulations using the MNIST handwritten digits dataset. This protocol achieves results comparable to Federated Learning with the FedAvg protocol while adding a layer of privacy to the models. Furthermore, it obtains a timing performance that does not significantly affect the total training time, unlike protocols that use Homomorphic Encryption. Full article
(This article belongs to the Special Issue Federated and Distributed Learning in IoT)
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20 pages, 682 KiB  
Article
Expand and Shrink: Federated Learning with Unlabeled Data Using Clustering
by Ajit Kumar, Ankit Kumar Singh, Syed Saqib Ali and Bong Jun Choi
Sensors 2023, 23(23), 9404; https://doi.org/10.3390/s23239404 - 25 Nov 2023
Viewed by 749
Abstract
The amalgamation of the Internet of Things (IoT) and federated learning (FL) is leading the next generation of data usage due to the possibility of deep learning with data privacy preservation. The FL architecture currently assumes labeled data samples from a client for [...] Read more.
The amalgamation of the Internet of Things (IoT) and federated learning (FL) is leading the next generation of data usage due to the possibility of deep learning with data privacy preservation. The FL architecture currently assumes labeled data samples from a client for supervised classification, which is unrealistic. Most research works in the literature focus on local training, update receiving, and global model updates. However, by principle, the labeling must be performed on the client side because the data samples cannot leave the source under the FL principle. In the literature, a few works have proposed methods for unlabeled data for FL using “class-prior probabilities” or “pseudo-labeling”. However, these methods make either unrealistic or uncommon assumptions, such as knowing class-prior probabilities are impractical or unavailable for each classification task and even more challenging in the IoT ecosystem. Considering these limitations, we explored the possibility of performing federated learning with unlabeled data by providing a clustering-based method of labeling the sample before training or federation. The proposed work will be suitable for every type of classification task. We performed different experiments on the client by varying the labeled data ratio, the number of clusters, and the client participation ratio. We achieved accuracy rates of 87% and 90% by using 0.01 and 0.03 of the truth labels, respectively. Full article
(This article belongs to the Special Issue Federated and Distributed Learning in IoT)
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17 pages, 3152 KiB  
Article
FedRAD: Heterogeneous Federated Learning via Relational Adaptive Distillation
by Jianwu Tang, Xuefeng Ding, Dasha Hu, Bing Guo, Yuncheng Shen, Pan Ma and Yuming Jiang
Sensors 2023, 23(14), 6518; https://doi.org/10.3390/s23146518 - 19 Jul 2023
Cited by 1 | Viewed by 1521
Abstract
As the development of the Internet of Things (IoT) continues, Federated Learning (FL) is gaining popularity as a distributed machine learning framework that does not compromise the data privacy of each participant. However, the data held by enterprises and factories in the IoT [...] Read more.
As the development of the Internet of Things (IoT) continues, Federated Learning (FL) is gaining popularity as a distributed machine learning framework that does not compromise the data privacy of each participant. However, the data held by enterprises and factories in the IoT often have different distribution properties (Non-IID), leading to poor results in their federated learning. This problem causes clients to forget about global knowledge during their local training phase and then tends to slow convergence and degrades accuracy. In this work, we propose a method named FedRAD, which is based on relational knowledge distillation that further enhances the mining of high-quality global knowledge by local models from a higher-dimensional perspective during their local training phase to better retain global knowledge and avoid forgetting. At the same time, we devise an entropy-wise adaptive weights module (EWAW) to better regulate the proportion of loss in single-sample knowledge distillation versus relational knowledge distillation so that students can weigh losses based on predicted entropy and learn global knowledge more effectively. A series of experiments on CIFAR10 and CIFAR100 show that FedRAD has better performance in terms of convergence speed and classification accuracy compared to other advanced FL methods. Full article
(This article belongs to the Special Issue Federated and Distributed Learning in IoT)
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35 pages, 6367 KiB  
Article
Botnet Detection and Mitigation Model for IoT Networks Using Federated Learning
by Francisco Lopes de Caldas Filho, Samuel Carlos Meneses Soares, Elder Oroski, Robson de Oliveira Albuquerque, Rafael Zerbini Alves da Mata, Fábio Lúcio Lopes de Mendonça and Rafael Timóteo de Sousa Júnior
Sensors 2023, 23(14), 6305; https://doi.org/10.3390/s23146305 - 11 Jul 2023
Cited by 7 | Viewed by 2235
Abstract
The Internet of Things (IoT) introduces significant security vulnerabilities, raising concerns about cyber-attacks. Attackers exploit these vulnerabilities to launch distributed denial-of-service (DDoS) attacks, compromising availability and causing financial damage to digital infrastructure. This study focuses on mitigating DDoS attacks in corporate local networks [...] Read more.
The Internet of Things (IoT) introduces significant security vulnerabilities, raising concerns about cyber-attacks. Attackers exploit these vulnerabilities to launch distributed denial-of-service (DDoS) attacks, compromising availability and causing financial damage to digital infrastructure. This study focuses on mitigating DDoS attacks in corporate local networks by developing a model that operates closer to the attack source. The model utilizes Host Intrusion Detection Systems (HIDS) to identify anomalous behaviors in IoT devices and employs network-based intrusion detection approaches through a Network Intrusion Detection System (NIDS) for comprehensive attack identification. Additionally, a Host Intrusion Detection and Prevention System (HIDPS) is implemented in a fog computing infrastructure for real-time and precise attack detection. The proposed model integrates NIDS with federated learning, allowing devices to locally analyze their data and contribute to the detection of anomalous traffic. The distributed architecture enhances security by preventing volumetric attack traffic from reaching internet service providers and destination servers. This research contributes to the advancement of cybersecurity in local network environments and strengthens the protection of IoT networks against malicious traffic. This work highlights the efficiency of using a federated training and detection procedure through deep learning to minimize the impact of a single point of failure (SPOF) and reduce the workload of each device, thus achieving accuracy of 89.753% during detection and increasing privacy issues in a decentralized IoT infrastructure with a near-real-time detection and mitigation system. Full article
(This article belongs to the Special Issue Federated and Distributed Learning in IoT)
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18 pages, 2000 KiB  
Article
Training of Classification Models via Federated Learning and Homomorphic Encryption
by Eduardo Angulo, José Márquez and Ricardo Villanueva-Polanco
Sensors 2023, 23(4), 1966; https://doi.org/10.3390/s23041966 - 09 Feb 2023
Cited by 2 | Viewed by 1733
Abstract
With the rise of social networks and the introduction of data protection laws, companies are training machine learning models using data generated locally by their users or customers in various types of devices. The data may include sensitive information such as family information, [...] Read more.
With the rise of social networks and the introduction of data protection laws, companies are training machine learning models using data generated locally by their users or customers in various types of devices. The data may include sensitive information such as family information, medical records, personal habits, or financial records that, if leaked, can generate problems. For this reason, this paper aims to introduce a protocol for training Multi-Layer Perceptron (MLP) neural networks via combining federated learning and homomorphic encryption, where the data are distributed in multiple clients, and the data privacy is preserved. This proposal was validated by running several simulations using a dataset for a multi-class classification problem, different MLP neural network architectures, and different numbers of participating clients. The results are shown for several metrics in the local and federated settings, and a comparative analysis is carried out. Additionally, the privacy guarantees of the proposal are formally analyzed under a set of defined assumptions, and the added value of the proposed protocol is identified compared with previous works in the same area of knowledge. Full article
(This article belongs to the Special Issue Federated and Distributed Learning in IoT)
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17 pages, 812 KiB  
Article
New Generation Federated Learning
by Boyuan Li, Shengbo Chen and Zihao Peng
Sensors 2022, 22(21), 8475; https://doi.org/10.3390/s22218475 - 03 Nov 2022
Cited by 2 | Viewed by 2179
Abstract
With the development of the Internet of things (IoT), federated learning (FL) has received increasing attention as a distributed machine learning (ML) framework that does not require data exchange. However, current FL frameworks follow an idealized setup in which the task size is [...] Read more.
With the development of the Internet of things (IoT), federated learning (FL) has received increasing attention as a distributed machine learning (ML) framework that does not require data exchange. However, current FL frameworks follow an idealized setup in which the task size is fixed and the storage space is unlimited, which is impossible in the real world. In fact, new classes of these participating clients always emerge over time, and some samples are overwritten or discarded due to storage limitations. We urgently need a new framework to adapt to the dynamic task sequences and strict storage constraints in the real world. Continuous learning or incremental learning is the ultimate goal of deep learning, and we introduce incremental learning into FL to describe a new federated learning framework. New generation federated learning (NGFL) is probably the most desirable framework for FL, in which, in addition to the basic task of training the server, each client needs to learn its private tasks, which arrive continuously independent of communication with the server. We give a rigorous mathematical representation of this framework, detail several major challenges faced under this framework, and address the main challenges of combining incremental learning with federated learning (aggregation of heterogeneous output layers and the task transformation mutual knowledge problem), and show the lower and upper baselines of the framework. Full article
(This article belongs to the Special Issue Federated and Distributed Learning in IoT)
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