Federated Learning: Applications and Future Directions

A special issue of Journal of Sensor and Actuator Networks (ISSN 2224-2708). This special issue belongs to the section "Big Data, Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 3695

Special Issue Editors


E-Mail Website
Guest Editor
Institute of High Performance Computing and Networking, National Research Council of Italy, 80131 Naples, Italy
Interests: federated learning; deep learning; signal processing
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Dipartimento di Matematica e Fisica, Università degli Studi della Campania “Luigi Vanvitelli”, 81100 Caserta, Italy
Interests: signal processing; artificial intelligence; predictive maintenance; digital twins
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Mathematics and Physics, Università della Campania “Luigi Vanvitelli”, Viale Lincoln, 81100 Caserta, Italy
Interests: artificial intelligence; machine and deep learning; federated deep learning on cloud systems; data analytics and data science applied to Internet of Things and cyber-physical systems; natural language processing
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Institute for High Performance Computing and Networking (ICAR), National Research Council (CNR), 80131 Naples, Italy
Interests: machine learning; deep learning; natural language processing; security; privacy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Federated learning (FL) addresses several relevant challenges in this space, and it has thus become an important research area in machine learning and AI. Federated learning can be used when one wants to train a machine learning model based on a dataset stored across multiple locations without the ability to move the data to any central location.

One class of applications relates to when data are generated by different smartphone app users, staying on users’ phones for privacy reasons. Another class of applications involves data collected by various organizations, which are unable to be shared due to confidentiality reasons. Nevertheless, the same restrictions can also be present independent of privacy concerns, such as in the case of data streams collected by IoT devices or self-driving cars, which need to be processed on the device because it is infeasible to transmit and store the sheer amount of data.

This Special Issue aims to collect several novel contributions and research experiences regarding federated learning studies and applications from different research communities concerning different but complementary solutions and proposals to mitigate issues and optimize Federated Learning algorithms.

The topics of the Special Issue include (but are not limited to):

  • Advances, novel issues, and open challenges in federated learning;
  • Federated learning trust policies and strategies;
  • Security concerns with federated learning;
  • Performance evaluation methods, metrics and tools of federated learning systems;
  • Performance optimization of federated learning models;
  • Privacy concerns and federated learning;
  • Case Studies and applications of federated learning;
  • Federated learning frameworks and tools employment and comparisons;
  • Federated learning and Blockchain;
  • Federated learning for IoT;
  • Federated learning for smart grids;
  • Federated learning for energy efficiency In IoT;
  • Federated learning for industrial applications;
  • Federated learning and graph-based approaches for fraud detection;
  • Federated learning for intrusion detection In IoT;
  • Federated learning with edge computing for cybersecurity In IoT;
  • Federated learning for privacy preservation of users in social media apps.

Dr. Giovanni Paragliola
Dr. Laura Verde
Dr. Fiammetta Marulli
Dr. Rosario Catelli
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. Journal of Sensor and Actuator Networks 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 2000 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
  • machine learning
  • IoT
  • Industry 4.0
  • security
  • edge
  • intrusion detection
  • blockchain
  • smart grids

Published Papers (2 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

19 pages, 3237 KiB  
Article
Reduction in Data Imbalance for Client-Side Training in Federated Learning for the Prediction of Stock Market Prices
by Momina Shaheen, Muhammad Shoaib Farooq and Tariq Umer
J. Sens. Actuator Netw. 2024, 13(1), 1; https://doi.org/10.3390/jsan13010001 - 21 Dec 2023
Cited by 1 | Viewed by 1449
Abstract
The approach of federated learning (FL) addresses significant challenges, including access rights, privacy, security, and the availability of diverse data. However, edge devices produce and collect data in a non-independent and identically distributed (non-IID) manner. Therefore, it is possible that the number of [...] Read more.
The approach of federated learning (FL) addresses significant challenges, including access rights, privacy, security, and the availability of diverse data. However, edge devices produce and collect data in a non-independent and identically distributed (non-IID) manner. Therefore, it is possible that the number of data samples may vary among the edge devices. This study elucidates an approach for implementing FL to achieve a balance between training accuracy and imbalanced data. This approach entails the implementation of data augmentation in data distribution by utilizing class estimation and by balancing on the client side during local training. Secondly, simple linear regression is utilized for model training at the client side to manage the optimal computation cost to achieve a reduction in computation cost. To validate the proposed approach, the technique was applied to a stock market dataset comprising stocks (AAL, ADBE, ASDK, and BSX) to predict the day-to-day values of stocks. The proposed approach has demonstrated favorable results, exhibiting a strong fit of 0.95 and above with a low error rate. The R-squared values, predominantly ranging from 0.97 to 0.98, indicate the model’s effectiveness in capturing variations in stock prices. Strong fits are observed within 75 to 80 iterations for stocks displaying consistently high R-squared values, signifying accuracy. On the 100th iteration, the declining MSE, MAE, and RMSE (AAL at 122.03, 4.89, 11.04, respectively; ADBE at 457.35, 17.79, and 21.38, respectively; ASDK at 182.78, 5.81, 13.51, respectively; and BSX at 34.50, 4.87, 5.87, respectively) values corroborated the positive results of the proposed approach with minimal data loss. Full article
(This article belongs to the Special Issue Federated Learning: Applications and Future Directions)
Show Figures

Figure 1

20 pages, 5774 KiB  
Article
A Federated Learning Approach to Support the Decision-Making Process for ICU Patients in a European Telemedicine Network
by Giovanni Paragliola, Patrizia Ribino and Zaib Ullah
J. Sens. Actuator Netw. 2023, 12(6), 78; https://doi.org/10.3390/jsan12060078 - 20 Nov 2023
Viewed by 1323
Abstract
A result of the pandemic is an urgent need for data collaborations that empower the clinical and scientific communities in responding to rapidly evolving global challenges. The ICU4Covid project joined research institutions, medical centers, and hospitals all around Europe in a telemedicine network [...] Read more.
A result of the pandemic is an urgent need for data collaborations that empower the clinical and scientific communities in responding to rapidly evolving global challenges. The ICU4Covid project joined research institutions, medical centers, and hospitals all around Europe in a telemedicine network for sharing capabilities, knowledge, and expertise distributed within the network. However, healthcare data sharing has ethical, regulatory, and legal complexities that pose several restrictions on their access and use. To mitigate this issue, the ICU4Covid project integrates a federated learning architecture, allowing distributed machine learning within a cross-institutional healthcare system without the data being transported or exposed outside their original location. This paper presents the federated learning approach to support the decision-making process for ICU patients in a European telemedicine network. The proposed approach was applied to the early identification of high-risk hypertensive patients. Experimental results show how the knowledge of every single node is spread within the federation, improving the ability of each node to make an early prediction of high-risk hypertensive patients. Moreover, a performance evaluation shows an accuracy and precision of over 90%, confirming a good performance of the FL approach as a prediction test. The FL approach can significantly support the decision-making process for ICU patients in distributed networks of federated healthcare organizations. Full article
(This article belongs to the Special Issue Federated Learning: Applications and Future Directions)
Show Figures

Figure 1

Back to TopTop