Privacy-preserving federated learning in healthcare: Fundamentals, state of the art and prospective research directions
- Title
- Privacy-preserving federated learning in healthcare: Fundamentals, state of the art and prospective research directions
- Creator
- Namitha T.N.; Raghavendra S.; Vinith R.
- Description
- Recent collaborations in medical diagnostic systems are based on data private collaborative learning using Federated Learning (FL). In this approach, multiple organizations train a machine-learning model at the same time eventually leading to global model generation. This paper reviews the fundamentals of FL and its evolution path in Healthcare. The objective of this review is to scope a wide variety of healthcare applications in FL. Exactly what research direction is moving in interesting for research communities to guide their future course. This review uniquely focuses on examining numerous FL-based healthcare implementations, detailing their core methodologies and performance metrics, which, to our knowledge, have not been previously available. Privacy-preserving collaborative distributed learning through federated learning in healthcare enhances research collaborations, thereby resulting in better-performing models. This comprehensive review will act as a valuable reference for researchers exploring new FL applications in the healthcare domain. 2024 IEEE.
- Source
- 2024 IEEE 3rd World Conference on Applied Intelligence and Computing, AIC 2024, pp. 1438-1443.
- Date
- 2024-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- Architecture; Collaborative learning; Federated Learning; Health care; Privacy Preservation
- Coverage
- Namitha T.N., Christ (Deemed to Be University), Department of Cse, Bangalore, India; Raghavendra S., Christ (Deemed to Be University), Department of Cse, Bangalore, India; Vinith R., Amrita Vishwa Vidyapeetham, School of Artificial Intelligence, Coimbatore, India
- Rights
- Restricted Access
- Relation
- ISBN: 979-835038459-8
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Namitha T.N.; Raghavendra S.; Vinith R., “Privacy-preserving federated learning in healthcare: Fundamentals, state of the art and prospective research directions,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 25, 2025, https://archives.christuniversity.in/items/show/19062.