This paper provides a comprehensive review of federated learning (FL) in healthcare, highlighting its potential to enhance data-driven healthcare solutions while addressing privacy and data security concerns. FL enables the training of large machine learning models across multiple data centers without sharing sensitive information, making it ideal for healthcare applications where data privacy is critical. The review discusses the challenges associated with healthcare data, including data quality, annotation errors, heterogeneity, privacy, and standard datasets. It also explores various FL algorithms, such as FedAvg, FedPer, FedMA, secret sharing, and homomorphic encryption, and evaluates their performance in terms of accuracy, efficiency, and privacy preservation. The study emphasizes the importance of secure aggregation methods in FL to ensure compliance with regulations like HIPAA and GDPR. The paper concludes that FL offers a promising approach for collaborative healthcare research, enabling the development of robust and privacy-preserving models. It also highlights the need for further research to address the challenges of data heterogeneity, model fairness, and the integration of FL into healthcare systems. The review serves as a foundation for future investigations into FL's potential in healthcare and provides a standard for protecting personally identifiable medical data.This paper provides a comprehensive review of federated learning (FL) in healthcare, highlighting its potential to enhance data-driven healthcare solutions while addressing privacy and data security concerns. FL enables the training of large machine learning models across multiple data centers without sharing sensitive information, making it ideal for healthcare applications where data privacy is critical. The review discusses the challenges associated with healthcare data, including data quality, annotation errors, heterogeneity, privacy, and standard datasets. It also explores various FL algorithms, such as FedAvg, FedPer, FedMA, secret sharing, and homomorphic encryption, and evaluates their performance in terms of accuracy, efficiency, and privacy preservation. The study emphasizes the importance of secure aggregation methods in FL to ensure compliance with regulations like HIPAA and GDPR. The paper concludes that FL offers a promising approach for collaborative healthcare research, enabling the development of robust and privacy-preserving models. It also highlights the need for further research to address the challenges of data heterogeneity, model fairness, and the integration of FL into healthcare systems. The review serves as a foundation for future investigations into FL's potential in healthcare and provides a standard for protecting personally identifiable medical data.