Advancing Pandemic Preparedness in Healthcare 5.0: A Survey of Federated Learning Applications

Table 8

Challenges and mitigation strategies for implementing FL in pandemic preparedness.

Challenges	Mitigation strategies
Data privacy	Employ encryption and secure communication protocols for data transmission
	Utilize differential privacy techniques to add noise to individual data
Data heterogeneity	Standardize data formats and feature representations across institutions
	Use transfer learning and data augmentation to handle variations in data quality
Data interoperability	Adopt standardized data schemas and APIs for seamless data exchange
	Implement data mapping and transformation techniques for interoperability
Trust and collaboration	Establish data sharing agreements and governance frameworks
	Utilize FL consortiums to build trust and promote collaboration
Model performance degradation	Employ advanced model aggregation methods to mitigate performance degradation
	Implement robustness checks to ensure model quality and consistency
Communication overhead	Use efficient communication protocols to reduce overhead
	Implement asynchronous communication for distributed model updates
Scalability and resource allocation	Optimize computation and memory resources for efficient model training
	Employ edge computing to reduce the burden on central servers
Regulatory and ethical compliance	Ensure compliance with data protection and ethical guidelines
	Obtain necessary approvals and permissions for cross-institutional data sharing