Abstract
In the realm of digital healthcare, there is a growing trend toward the utilization of exciting technologies such as federated learning (FL) and artificial intelligence. Healthcare systems previously focused on centralized working must now collaborate with the use of remarkable FL. Multiple collaborations can be achieved in a distributed manner where collaborators can communicate when desired in a centralized fashion without sharing the raw data. FL deals with challenges and concerns related to data confidentiality and privacy, helping in minimizing the risk of confidential data leakage. In this chapter, we survey and discuss various literature related to the use of FL in digital healthcare systems. We state the various challenges that arise in the digital healthcare system and provide case studies and solutions to the problem with the use of FL. Finally, we conclude with open challenges that have yet to be addressed.
| Original language | English |
|---|---|
| Title of host publication | Federated Learning for Digital Healthcare Systems |
| Publisher | Elsevier |
| Pages | 301-323 |
| Number of pages | 23 |
| ISBN (Electronic) | 9780443138973 |
| ISBN (Print) | 9780443138966 |
| DOIs | |
| State | Published - 1 Jan 2024 |
Keywords
- Artificial intelligence
- data privacy and security
- digital healthcare system
- federated learning
- secured model sharing