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Advantages | Strengths | Limitations |
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Data privacy protection | FL ensures privacy by not sharing raw patient data | Conventional methods often require sharing sensitive healthcare data |
Only aggregated model updates are exchanged among institutions | Patient data privacy concerns can pose ethical and legal challenges |
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Decentralized | FL empowers local decision-making, adapting to localized outbreaks | Traditional centralized systems may lead to slow and less adaptive decisions |
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Decision-making | Institutions or regions can make data-driven decisions based on local insights | Delays in data processing can hinder critical decision-making during a pandemic |
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Real-time surveillance | FL allows real-time insights as models are updated collaboratively | Data aggregation and analysis in traditional methods can be time-consuming |
Institutions can continuously update models, providing up-to-the-minute data | Delays in data processing can hinder critical decision-making during a pandemic |
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Resource-constrained environments | FL accommodates resource-constrained environments | Traditional methods may require substantial infrastructure and data centres |
Enables collaboration among institutions with varying resources | Institutions with limited resources may face barriers to participation |
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Research collaboration | FL fosters global research collaboration by allowing data analysis without data sharing | Traditional research often involves data silos and barriers to data sharing |
Researchers worldwide can collaborate on diverse datasets without compromising privacy | Data sharing limitations can hinder international research efforts |
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