Federated learning is also called collaborative learning which uses the decentralized approach to train the machine learning models. Federated learning, a groundbreaking approach in the field of machine learning, has immense potential to transform healthcare as we know it. By harnessing the power of distributed facts from IoT sensors and devices, federated learning enables healthcare providers to train AI models without compromising patient privacy. Key assistances of federated learning in health care are its skill to overcome data sharing limitations. In traditional approaches, sensitive patient data must be centralized for training AI models. However, with federated learning, hospitals and clinics can keep their data secure within their own premises while contributing to a collective intelligence.
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Unlocking data to fuel new AI applications, federated learning (FL) is a decentralized machine learning technique that is used to train AI models without anyone seeing or manipulating the data. With datasets scattered among data centers like hospitals, clinical research labs, and mobile devices, FL is the process of building machine learning models while preventing data loss. This overview examines earlier research and studies on federated learning in the healthcare sector across a range of use cases and applications (Wen et al., 2022). This study emphasizes federated learning's problems, fixes, and applications that practitioners need to be aware of.
The most well-known and fascinating technologies in the field of intelligent healthcare are federated learning (FL), explainable artificial intelligence (XAI), and artificial intelligence (AI) (Boesch, n.d). A centralized network of agents exchanging raw data was the foundation upon which the healthcare system in the past functioned. This system still has numerous flaws and issues as a result. Instead, the system consists of many AI-powered agent collaborators that can effectively communicate with the host of their choice. Another noteworthy feature is FL, which is decentralized and maintains communication in the chosen system based on a model without transferring raw data. By combining FL, AI, and XAI techniques, many limitations and challenges in the healthcare system may be lessened.
Healthcare has advanced rapidly thanks to artificial intelligence (AI), and significant strides have been made in treating a variety of challenging medical conditions. Nonetheless, there aren't any standards for patient electronic medical records, and there aren't any rules of conduct or law governing the confidentiality of patient data (Issa et al., 2023). The extensive application of AI in the healthcare industry has been hampered by the fragmentation of medical data. To solve the issue of data fragmentation, federated learning has emerged. When used in conjunction with privacy-preserving algorithms, it can significantly reduce privacy problems. Block chain, edge computing, and other technologies can also be used with federated learning to increase security and computational effectiveness.
The incorporation of multiple technologies, such as IoT, has led to a significant development in intelligent applications in distributed healthcare and medical systems in recent years. Furthermore, heterogeneous federated learning offers data transmission, replica placement, throughput reduction, resource management, and network load reduction to improve the accuracy, consistency, and service level agreement (SLA) components of public health and medical systems. In medical IoT (MIoT) applications including smart healthcare, smart surgery, smart medical systems, smart health transportation, and smart telemedicine, FL can be utilized in conjunction with machine learning and deep learning. Federated learning can be used in the following areas such as
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Federated learning for managing consistency in distributed medical systems with IoT
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Collaborative learning for smart healthcare and smart surgery
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Privacy, security, and trust in federated learning for distributed medical systems in IoT environments
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Federated-iterative learning methods for service management of medical systems
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Federated learning and machine learning for optimizing cloud-edge computing in medical IoT
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Analyzing QoS factors in federated learning-based resource management in MIoT
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Federated learning applications for energy-aware systems in heterogeneous medical systems
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Federated learning techniques for medical image processing in IoT environments
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Blockchain technology using federated learning methods in medical IoT environments
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Deep learning for MRI and X-ray in mobile medical IoT environments
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Robust machine learning models for smart healthcare in IoT environments