-
A survey on securing federated learning [Elektronski vir] : analysis of applications, attacks, challenges, and trendsCunha Neto, Helio N. ...The growth of data generation capabilities, facilitated by advancements in communication and computation technologies, as well as the rise of the Internet of Things (IoT), results in vast amounts of ... data that significantly enhance the performance of machine learning models. However, collecting all necessary data to train accurate models is often unfeasible due to privacy laws. Federated Learning (FL) evolved as a collaborative machine learning approach for training models without sharing private data. Unfortunately, several in-design vulnerabilities have been exposed, allowing attackers to infer private data of participants and negatively impacting the performance of the federated model. In light of these challenges and to encourage the development of FL solutions, this paper provides a comprehensive analysis of secure FL proposals that both protect user privacy and enhance the performance of the model. We performed a systematic review using predefined criteria to screen and extract data from multiple electronic databases, resulting in a final set of studies for analysis. Through the systematic review methodology, the paper groups the security vulnerabilities of FL into model performance and data privacy attacks. It also presents an analysis and comparison of potential mitigation strategies against these attacks. Additionally, the paper conducts a security analysis of state-of-the-art FL applications and proposals based on the vulnerabilities addressed. Finally, the paper outlines the main applications of secure FL and lists future research challenges. The survey highlights the crucial role of security strategies in ensuring the protection of user privacy and model performance in the context of future FL applications.Source: IEEE access [Elektronski vir]. - ISSN 2169-3536 (Vol. 11, 2023, str. 41928-41953)Type of material - e-article ; adult, seriousPublish date - 2023Language - englishCOBISS.SI-ID - 159983619
Author
Cunha Neto, Helio N. |
Hribar, Jernej, telekomunikacije, 1989- |
Dusparič, Ivana |
Ferrazani Mattos, Diogo Menezes |
Fernandes, Natalia C.
Topics
Strojno učenje |
federativno učenje |
skupinsko učenje |
varnost informacij |
večdostopno računalništvo na robu |
federated learning |
machine learning |
collaborative learning |
information security |
multi-access edge computing
Shelf entry
Permalink
- URL:
Impact factor
Access to the JCR database is permitted only to users from Slovenia. Your current IP address is not on the list of IP addresses with access permission, and authentication with the relevant AAI accout is required.
Year | Impact factor | Edition | Category | Classification | ||||
---|---|---|---|---|---|---|---|---|
JCR | SNIP | JCR | SNIP | JCR | SNIP | JCR | SNIP |
Select the library membership card:
DRS, in which the journal is indexed
Database name | Field | Year |
---|
Links to authors' personal bibliographies | Links to information on researchers in the SICRIS system |
---|---|
Cunha Neto, Helio N. | |
Hribar, Jernej, telekomunikacije, 1989- | 56214 |
Dusparič, Ivana | |
Ferrazani Mattos, Diogo Menezes | |
Fernandes, Natalia C. |
Select pickup location:
Material pickup by post
Notification
Select pickup location
Pickup location | Material status | Reservation |
---|
Please wait a moment.