Federated Learning in Data Privacy and Security
Federated learning (FL) has been a rapidly growing topic in recent years. The biggest concern in federated learning is data privacy and cybersecurity. There are many algorithms that federated models have to work on to achieve greater efficiency, security, quality and effective learning. This paper f...
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| Format: | Article |
| Language: | English |
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Ediciones Universidad de Salamanca
2024-12-01
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| Series: | Advances in Distributed Computing and Artificial Intelligence Journal |
| Subjects: | |
| Online Access: | https://revistas.usal.es/cinco/index.php/2255-2863/article/view/31647 |
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| author | Dokuru Trisha Reddy Haripriya Nandigam Sai Charan Indla S. P. Raja |
| author_facet | Dokuru Trisha Reddy Haripriya Nandigam Sai Charan Indla S. P. Raja |
| author_sort | Dokuru Trisha Reddy |
| collection | DOAJ |
| description | Federated learning (FL) has been a rapidly growing topic in recent years. The biggest concern in federated learning is data privacy and cybersecurity. There are many algorithms that federated models have to work on to achieve greater efficiency, security, quality and effective learning. This paper focuses on algorithms such as, federated averaging algorithm, differential privacy, federated stochastic variance and reduced gradient (FSVRG). To achieve data privacy and security, this research paper presents the main data statistics with the help of graphs, visual images and design models. Later, data security in federated learning models is researched and case studies are presented to identify risks and possible solutions. Detecting security gaps is a challenge for many companies. This paper presents solutions for the identification of security-related issues which results in a decrease in time complexity and an increase in accuracy. This research sheds light on the topics of federated learning and data security. |
| format | Article |
| id | doaj-art-877c56a0b2c84c8086615a6ab78cc620 |
| institution | Kabale University |
| issn | 2255-2863 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Ediciones Universidad de Salamanca |
| record_format | Article |
| series | Advances in Distributed Computing and Artificial Intelligence Journal |
| spelling | doaj-art-877c56a0b2c84c8086615a6ab78cc6202025-01-23T11:25:18ZengEdiciones Universidad de SalamancaAdvances in Distributed Computing and Artificial Intelligence Journal2255-28632024-12-0113e31647e3164710.14201/adcaij.3164737128Federated Learning in Data Privacy and SecurityDokuru Trisha Reddy0Haripriya Nandigam1Sai Charan Indla2S. P. Raja3School of Computer Science and Engineering, Vellore Institute of Technology, Vellore Campus, Tiruvalam Rd, Katpadi, Vellore, Tamil Nadu 632014, IndiaSchool of Computer Science and Engineering, Vellore Institute of Technology, Vellore Campus, Tiruvalam Rd, Katpadi, Vellore, Tamil Nadu 632014, IndiaSchool of Computer Science and Engineering, Vellore Institute of Technology, Vellore Campus, Tiruvalam Rd, Katpadi, Vellore, Tamil Nadu 632014, IndiaSchool of Computer Science and Engineering, Vellore Institute of Technology, Vellore Campus, Tiruvalam Rd, Katpadi, Vellore, Tamil Nadu 632014, IndiaFederated learning (FL) has been a rapidly growing topic in recent years. The biggest concern in federated learning is data privacy and cybersecurity. There are many algorithms that federated models have to work on to achieve greater efficiency, security, quality and effective learning. This paper focuses on algorithms such as, federated averaging algorithm, differential privacy, federated stochastic variance and reduced gradient (FSVRG). To achieve data privacy and security, this research paper presents the main data statistics with the help of graphs, visual images and design models. Later, data security in federated learning models is researched and case studies are presented to identify risks and possible solutions. Detecting security gaps is a challenge for many companies. This paper presents solutions for the identification of security-related issues which results in a decrease in time complexity and an increase in accuracy. This research sheds light on the topics of federated learning and data security.https://revistas.usal.es/cinco/index.php/2255-2863/article/view/31647flfederated averagingdifferential privacyfsvrg |
| spellingShingle | Dokuru Trisha Reddy Haripriya Nandigam Sai Charan Indla S. P. Raja Federated Learning in Data Privacy and Security Advances in Distributed Computing and Artificial Intelligence Journal fl federated averaging differential privacy fsvrg |
| title | Federated Learning in Data Privacy and Security |
| title_full | Federated Learning in Data Privacy and Security |
| title_fullStr | Federated Learning in Data Privacy and Security |
| title_full_unstemmed | Federated Learning in Data Privacy and Security |
| title_short | Federated Learning in Data Privacy and Security |
| title_sort | federated learning in data privacy and security |
| topic | fl federated averaging differential privacy fsvrg |
| url | https://revistas.usal.es/cinco/index.php/2255-2863/article/view/31647 |
| work_keys_str_mv | AT dokurutrishareddy federatedlearningindataprivacyandsecurity AT haripriyanandigam federatedlearningindataprivacyandsecurity AT saicharanindla federatedlearningindataprivacyandsecurity AT spraja federatedlearningindataprivacyandsecurity |