The article "Federated Learning for Healthcare: A Comprehensive Review" by Pallavi Dhade and Prajakta Shirke explores the application of federated learning (FL) in healthcare settings. Deep learning models have shown great promise in medical applications, such as text identification in laboratory reports, tumor segmentation from MRI scans, and cancer diagnosis. However, the centralized aggregation of clinical data raises concerns about privacy, data ownership, and legal restrictions. FL addresses these issues by enabling the training of large machine learning models across multiple data centers without sharing sensitive information. The authors review recent findings from a systematic literature review, highlighting the current state of research and practical implementations of FL in healthcare. They discuss the benefits of FL in maintaining privacy and data security while harnessing collective knowledge from multiple healthcare centers. The article also covers the challenges associated with healthcare data, such as data quality, incorrect data annotation, data heterogeneity, privacy and security, and the lack of standard medical datasets. The review includes a detailed analysis of various FL algorithms, including Federated Averaging (FedAvg), Federated Personalization (FedPer), Federated Match Averaging (FedMA), Secret Sharing, and Homomorphic Encryption, comparing their performance and privacy preservation capabilities. The authors conclude that FL has the potential to enhance patient and institutional access to high-quality healthcare while protecting sensitive patient data.The article "Federated Learning for Healthcare: A Comprehensive Review" by Pallavi Dhade and Prajakta Shirke explores the application of federated learning (FL) in healthcare settings. Deep learning models have shown great promise in medical applications, such as text identification in laboratory reports, tumor segmentation from MRI scans, and cancer diagnosis. However, the centralized aggregation of clinical data raises concerns about privacy, data ownership, and legal restrictions. FL addresses these issues by enabling the training of large machine learning models across multiple data centers without sharing sensitive information. The authors review recent findings from a systematic literature review, highlighting the current state of research and practical implementations of FL in healthcare. They discuss the benefits of FL in maintaining privacy and data security while harnessing collective knowledge from multiple healthcare centers. The article also covers the challenges associated with healthcare data, such as data quality, incorrect data annotation, data heterogeneity, privacy and security, and the lack of standard medical datasets. The review includes a detailed analysis of various FL algorithms, including Federated Averaging (FedAvg), Federated Personalization (FedPer), Federated Match Averaging (FedMA), Secret Sharing, and Homomorphic Encryption, comparing their performance and privacy preservation capabilities. The authors conclude that FL has the potential to enhance patient and institutional access to high-quality healthcare while protecting sensitive patient data.