Secure and Private Federated Learning through Encrypted Parameter Aggregation
- Title
- Secure and Private Federated Learning through Encrypted Parameter Aggregation
- Creator
- Vijayalakshmi K.; Sitharselvam P.M.; Thamarai I.; Ashok J.; Sathish G.; Mayakannan S.
- Description
- This chapter is dedicated to cross-silo private parameter aggregation. ML/DL has demonstrated promising results in a variety of application domains, especially when vast volumes of data are collected in one location, such as a data center or a cloud service. The goal of FL is to improve the quality of ML/DL models while minimizing their drawbacks. Participating devices in an FL task could range in size from a single smartphone or watch to a global corporation housing multiple data centers. It was originally believed that just a little amount of information about the original training data would be carried over into subsequent model updates as FL interactions occurred. The differential privacy framework is concerned with restricting the release of private information while sharing the outcomes of computations or queries performed on a dataset. Recently, many researchers have begun to employ differential privacy while training models in a federated setting. 2024 Saravanan Krishnan, A. Jose Anand, R. Srinivasan, R. Kavitha and S. Suresh.
- Source
- Handbook on Federated Learning: Advances, Applications and Opportunities, pp. 80-105.
- Date
- 2024-01-01
- Publisher
- CRC Press
- Coverage
- Vijayalakshmi K., Department of Electrical and Communication Engineering, College of Engineering, National University of Science and Technology, Muscat, Oman; Sitharselvam P.M., RVS Educational Trusts Group of Institutions, Tamilnadu, Dindigul, India; Thamarai I., Department of Computer Science and Engineering, Panimalar Engineering College, Chennai City Campus, Tamil Nadu, Chennai, 600029, India; Ashok J., School of Business and Management, CHRIST (Deemed to be University), Karnataka, Bengaluru, India; Sathish G., Department of Information Technology, St. Martins Engineering College, Telangana, Secunderabad, India; Mayakannan S., Department of Mechanical Engineering, Vidyaa Vikas College of Engineering and Technology, Tiruchengode, Namakkal, India
- Rights
- Restricted Access
- Relation
- ISBN: 978-100383750-3; 978-103247162-4
- Format
- Online
- Language
- English
- Type
- Book chapter
Collection
Citation
Vijayalakshmi K.; Sitharselvam P.M.; Thamarai I.; Ashok J.; Sathish G.; Mayakannan S., “Secure and Private Federated Learning through Encrypted Parameter Aggregation,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 23, 2025, https://archives.christuniversity.in/items/show/18152.