What are the main opportunities of persinalized federated learning relative to the federated learning in healthcare?
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Personalized federated learning offers significant advantages over traditional federated learning in healthcare. By integrating blockchain and federated learning, healthcare systems can securely share medical records while maintaining data privacy. Additionally, personalized federated learning addresses challenges like imbalanced datasets and intermittent clients in decentralized healthcare systems. In the context of remote patient health monitoring using IoT devices, federated learning ensures data privacy by training ML models locally without transferring sensitive information to the cloud. Moreover, the proposed Polymorphic Cipher Functional Encryption method enhances privacy and accuracy in federated learning systems, particularly in healthcare applications. These advancements in personalized federated learning not only safeguard patient data but also improve model performance and collaboration among healthcare institutions.
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| Papers (5) | Insight |
|---|---|
| Personalized federated learning offers enhanced privacy protection and model accuracy by integrating Polymorphic Cipher Functional Encryption, minimizing noise infusion, and maintaining trust, as demonstrated in healthcare data protection. | |
| Personalized federated learning offers tailored healthcare predictions by training models locally on IoT devices, enhancing patient privacy and data security compared to traditional federated learning in healthcare. | |
| Personalized federated learning in healthcare offers tailored model training based on individual data, enhancing privacy and accuracy, compared to traditional federated learning, enabling more effective and secure healthcare AI advancements. | |
| Personalized federated learning offers enhanced privacy and accuracy by tailoring models to individual clients, enabling collaborative healthcare systems with intermittent clients and imbalanced datasets, as highlighted in the paper. | |
24 Jun 2023 | Personalized federated learning offers tailored model training for individual data without centralizing it, enhancing privacy and customization, crucial in healthcare for patient-specific insights while maintaining data security. |
Related Questions
What is the most cited article on federated learning and health?5 answersThe most cited article on federated learning and health is a study that focuses on the application of federated learning in healthcare environments. This research explores the challenges and benefits of training deep learning models using federated learning compared to traditional centralized methods. It highlights the effectiveness of federated learning in training models on distributed healthcare data while ensuring improved data privacy and security. Additionally, the study emphasizes the importance of addressing issues like domain adaptation and data heterogeneity to achieve optimal performance in federated learning. The findings underscore the potential of federated learning to revolutionize healthcare data analysis and model training, paving the way for enhanced patient privacy and collaborative research across multiple healthcare providers.
What are the challenges of federated learning for healthcare?5 answersFederated learning (FL) in healthcare faces several challenges. One major challenge is the limited availability of data, especially in fields like healthcare where vast amounts of data are required for training AI models. Another challenge is the privacy concerns associated with sharing medical records. Healthcare data is typically scattered across various sources, and these sources are often hesitant to share data due to privacy considerations. Malicious coordination of ML algorithms can also pose a threat to patient data confidentiality in FL-enabled IoT systems for health monitoring. Additionally, the integration of emerging technologies in healthcare, such as AI, cloud edge computing, IoT, and blockchain, brings new challenges in terms of interoperability, data management, and security. Overcoming these challenges is crucial for the successful implementation of FL in healthcare systems.
How can federated learning be used to improve healthcare outcomes?5 answersFederated learning can be used to improve healthcare outcomes by addressing the challenges of data privacy and security, as well as data heterogeneity and domain adaptation. It allows for the training of deep learning models on distributed healthcare data, providing similar performance to traditional methods while ensuring improved data privacy and security. By training ML models locally on IoT devices without transferring data to the cloud, federated learning enables information sharing among all IoT devices in hospitals, facilitating collaborative ML model training. Additionally, federated learning can reduce the time overhead of system running and authenticate user devices uploading data, enhancing the efficiency and security of the system. Overall, federated learning has the potential to transform healthcare by enabling the development of new ML-powered applications that enhance the quality of care, lower costs, and improve patient outcomes.
What scenario Federated Learning can use?5 answersFederated Learning can be used in scenarios where there is distributed data and the goal is to train machine learning or deep learning models while protecting data privacy. It is particularly effective in addressing network training under local data heterogeneity and can improve the speed of model aggregation by taking similarity into account as an influence factor. Additionally, Federated Learning can be applied in real-world scenarios with rapidly changing environments and heterogeneous hardware settings, where a synchronous protocol may be inflexible. Asynchronous Federated Learning combined with a novel asynchronous model aggregation protocol has been shown to significantly improve prediction performance while maintaining the same level of accuracy as centralized machine learning. Furthermore, Federated Learning can be used in extensive heterogeneous settings, providing excellent convergence speed, accuracy, and computation/communication efficiency.
What are the advantages and disadvantages of federated learning compared to centralized learning?5 answersFederated learning offers advantages over centralized learning in terms of reducing costs and privacy concerns by leveraging local computational power and distributing training data across devices. It also preserves data privacy by employing privacy-preserving mechanisms during storage, transfer, and sharing. However, federated learning can suffer from data leakage due to the lack of privacy-preserving mechanisms, posing risks to data owners and suppliers. In addition, federated learning faces challenges in achieving fairness among participants, as some participants may short join the training process, resulting in unfairness to those who contributed more. On the other hand, centralized learning faces constraints in data mapping and security, making it difficult to carry out. Overall, federated learning offers benefits in terms of privacy and cost reduction, but it also presents challenges related to data leakage and fairness among participants.
What are the most recent proposed work of federated learning methods?5 answersFederated Learning is a popular method for training neural networks on distributed datasets. Recent proposed work in federated learning includes the introduction of Centered Kernel Alignment (CKA) into the loss function to compute the similarity of feature maps in the output layer, resulting in faster model aggregation and improved global model accuracy in non-IID scenarios. Another recent approach involves using structured variational inference, adapted for the federated setting, to enable model training across distributed data sources without data leaving their original locations. Additionally, a secure federated graph learning system called S-Glint has been designed to tackle the challenge of communication bottlenecks in federated graph learning, achieving better performance than existing solutions. Finally, a novel federated learning method has been developed for imbalanced data by directly optimizing the area under curve (AUC) score, with favorable theoretical results and efficacy demonstrated through extensive experiments.