Data Augmentation for Improving Convergence Speed in Federated Sequential Recommendation System
A federated sequential recommendation system enables personalized temporal recommendations while safeguarding user privacy. However, the statistical heterogeneity of independent user records often necessitates extensive communication to achieve high-performing models. To address this challenge, prio...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11086593/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849771574526214144 |
|---|---|
| author | Donghoon Lee Hyunsouk Cho |
| author_facet | Donghoon Lee Hyunsouk Cho |
| author_sort | Donghoon Lee |
| collection | DOAJ |
| description | A federated sequential recommendation system enables personalized temporal recommendations while safeguarding user privacy. However, the statistical heterogeneity of independent user records often necessitates extensive communication to achieve high-performing models. To address this challenge, prior research in other domains has employed data augmentation techniques to mitigate heterogeneity by generating synthetic datasets. Despite their potential, data augmentations have not been systematically explored in the context of federated recommendation systems. We aim to systematically evaluate six data augmentation methods and their effectiveness in mitigating statistical heterogeneity for efficient federated sequential recommendation. Our findings indicate that augmentation techniques introducing variation in sequence lengths can enhance convergence speed and improve the generalizability of federated models, while reducing communication overhead. To our knowledge, this is the first study to systematically evaluate data augmentation in federated recommendation systems. |
| format | Article |
| id | doaj-art-e1ba608def4c4dcc84fb795a25e50b8a |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-e1ba608def4c4dcc84fb795a25e50b8a2025-08-20T03:02:35ZengIEEEIEEE Access2169-35362025-01-011313239813240910.1109/ACCESS.2025.359076211086593Data Augmentation for Improving Convergence Speed in Federated Sequential Recommendation SystemDonghoon Lee0https://orcid.org/0000-0001-7435-9567Hyunsouk Cho1https://orcid.org/0000-0002-9134-1921Department of Artificial Intelligence, Ajou University, Suwon-si, Gyeonggi-do, Republic of KoreaDepartment of Artificial Intelligence, Ajou University, Suwon-si, Gyeonggi-do, Republic of KoreaA federated sequential recommendation system enables personalized temporal recommendations while safeguarding user privacy. However, the statistical heterogeneity of independent user records often necessitates extensive communication to achieve high-performing models. To address this challenge, prior research in other domains has employed data augmentation techniques to mitigate heterogeneity by generating synthetic datasets. Despite their potential, data augmentations have not been systematically explored in the context of federated recommendation systems. We aim to systematically evaluate six data augmentation methods and their effectiveness in mitigating statistical heterogeneity for efficient federated sequential recommendation. Our findings indicate that augmentation techniques introducing variation in sequence lengths can enhance convergence speed and improve the generalizability of federated models, while reducing communication overhead. To our knowledge, this is the first study to systematically evaluate data augmentation in federated recommendation systems.https://ieeexplore.ieee.org/document/11086593/Data miningdistributed computingrecommender systems |
| spellingShingle | Donghoon Lee Hyunsouk Cho Data Augmentation for Improving Convergence Speed in Federated Sequential Recommendation System IEEE Access Data mining distributed computing recommender systems |
| title | Data Augmentation for Improving Convergence Speed in Federated Sequential Recommendation System |
| title_full | Data Augmentation for Improving Convergence Speed in Federated Sequential Recommendation System |
| title_fullStr | Data Augmentation for Improving Convergence Speed in Federated Sequential Recommendation System |
| title_full_unstemmed | Data Augmentation for Improving Convergence Speed in Federated Sequential Recommendation System |
| title_short | Data Augmentation for Improving Convergence Speed in Federated Sequential Recommendation System |
| title_sort | data augmentation for improving convergence speed in federated sequential recommendation system |
| topic | Data mining distributed computing recommender systems |
| url | https://ieeexplore.ieee.org/document/11086593/ |
| work_keys_str_mv | AT donghoonlee dataaugmentationforimprovingconvergencespeedinfederatedsequentialrecommendationsystem AT hyunsoukcho dataaugmentationforimprovingconvergencespeedinfederatedsequentialrecommendationsystem |