Research Article
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 |
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