A Comprehensive Survey on Federated Learning Techniques for Healthcare Informatics

Table 1

Summary of Important surveys on FL in healthcare applications.

Ref.	Applications/use cases	Requirements/vision	Technicalchallenges	Enablingtechnologies	Researchdirections	Remarks
[30]	Low	Medium	Medium	Low	Low	Focused mainly on how FL can be applied in healthcare
[31]	Low	High	Medium	High	Low	(i) Focused mainly on FL initiatives related to digital health
						(ii) It highlights FedAvg and FedProx algorithms
[11]	Medium	Medium	High	High	High	Focused mainly on the cur- rent state of FL, including but not limited to the healthcare sector
[32]	HIGH	HIGH	MEDIUM	HIGH	HIGH	Focused mainly on advanced FL designs that would be useful for federated smart healthcare, such as; (i) Federated EHRs management(ii) Federated remote health monitoring; (iii) Federated medical imaging, and; (iv) Federated COVID-19 detection
[33]	HIGH	MEDIUM	MEDIUM	HIGH	HIGH	Focused mainly on the systematic literature review of current research about FL in the context of EHR data for healthcare applications
This paper	HIGH	HIGH	High	High	High	A comprehensive survey of FL applications for medical image processing, FL toward privacy and security in healthcare applications, FL in IoT-based smart healthcare applications and FL for outbreak prediction, technical challenges, enabling technologies and future research directions