Introduction-to-Federated-Learning (1).pptx

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1. FEDERATED LEARNING FOR MEDICAL IMAGING SUBMITTED BY : Group No. 10 Guide Name – Dr Rashmi Ranjan Sahoo Ankita Patra(2101109131) Ansuman Simanta Shekar Bhujabala(2101109132) Hritik Dash(2101109143) Purnima Prusty(2101109185)

2. Introduction to Federated Learning Federated learning is a machine learning technique that trains models on decentralized data. It allows devices to collaborate without sharing their data.

3. Limitations of Using Deep Learning Approaches in Medical Imaging

4. Benefits of using Federated Learning for medical imaging Data Privacy Devices don't need to share their raw data with each other or a central server. Data Security It reduces the risk of data breaches or unauthorized access to sensitive information. Scalability It can effectively train models on massive datasets distributed across numerous devices. Flexibility It allows models to be trained on data from different sources and domains.

5. LITERATURE SURVEY SL NO. TITLE YEAR OF PUBLICATION JOURNEL AUTHOR TOPIC 1 Federated Learning for Healthcare Informatics 2020 IEEE Transactions on Artificial Intelligence Andrew R. Willis, Juncheng Li, Tianlong Chen, Zhangyang Wang The paper discusses how federated learning can be applied to healthcare data to improve machine learning models while maintaining patient privacy. It covers methodologies, case studies, and potential challenges. 2 Federated Learning in Medical Imaging: A Survey 2021 Medical Image Analysis Daniel Rueckert, Jo Schlemper, Christian Baumgartner This survey explores the applications of federated learning in medical imaging, including segmentation, classification, and prediction tasks. It reviews existing literature, highlights the benefits of federated approaches, and discusses the future directions of this field.

6. SL NO. TITLE YEAR OF PUBLICATION JOURNEL AUTHOR TOPIC 3 Privacy-Preserving Machine Learning for Medical Image Analysis 2022 Nature Machine Intelligence He He, Murali Annavaram, Salman Avestimehr The article examines how federated learning can be used to perform machine learning on medical images while preserving patient privacy. It presents various techniques for secure data sharing and processing in federated learning environments. 4 Federated Learning for Precision Medicine: Challenges and Opportunities 2021 Journal of the American Medical Informatics Association (JAMIA) Yang Liu, Qiang Yang, Yin Zhang This paper addresses the application of federated learning in precision medicine, emphasizing the opportunities it presents for collaborative research and personalized healthcare while protecting patient data privacy. 5 Enabling Collaborative Learning in Healthcare Using Blockchain-Based Federated Learning 2022 IEEE Journal of Biomedical and Health Informatics Xuebin Ren, Jie Xu, Jianyong Wang The study explores the integration of blockchain technology with federated learning to enhance data security and integrity in collaborative healthcare research. It provides a framework for implementing such a system and discusses potential use cases and benefits.

7. Federated Learning Algorithms Federated Averaging This is a basic algorithm where devices compute model updates locally and then send them to a central server to be averaged. Federated Stochastic Gradient Descent This algorithm uses stochastic gradient descent to update models iteratively, but with decentralized data. It's similar to Federated Averaging, but with additional features for efficiency and performance. Federated Proximal This algorithm is used for constrained optimization problems, ensuring that the model updates remain within a defined constraint.

8. Federated Averaging 1 Local Training Each device trains a model on its own data. 2 Model Aggregation A central server collects the model updates from all devices. 3 Global Model Update The server averages the model updates and distributes the new model to the devices.

9. Federated Stochastic Gradient Descent 1 Stochastic Updates Each device uses stochastic gradient descent to update the model, using only a subset of its data. 2 Noise Reduction The algorithm introduces techniques like momentum and adaptive learning rates to reduce the noise in the stochastic updates. 3 Communication Efficiency It optimizes communication by sending smaller updates to the server. 4 Convergence The algorithm aims to converge to a globally optimal model, even with decentralized data.

10. Federated Proximal Problem Setup The problem involves minimizing a loss function while satisfying certain constraints. Local Updates Each device computes a local update that minimizes the loss function, subject to its local constraints. Server Aggregation The server aggregates the local updates and applies a projection operator to ensure that the global model satisfies the overall constraints. Model Distribution The server distributes the updated model to all devices for further local training.

11. Applications of Federated Learning Healthcare Train models for disease prediction, personalized medicine, and medical imaging. Mobile Devices Improve device performance, personalize user experiences, and detect fraud. Finance Develop fraud detection systems, risk assessment models, and personalized financial advice. Internet of Things Optimize resource allocation, predict equipment failures, and enhance user experiences.

12. Federated Learning Frameworks TensorFlow Federated A framework designed for federated learning, providing tools for model development, training, and evaluation. Flower An open-source framework for decentralized machine learning, with a focus on flexibility and customization.

13. TensorFlow Federated Codebase Provides a high-level API for creating and training federated learning models. Scalability Handles large-scale federated learning scenarios with numerous devices and datasets. Visualization Provides tools for visualizing the training process and model performance. Analysis Offers mechanisms for analyzing the training data and evaluating the model's performance.

14. Flower Flexibility Supports various federated learning strategies and custom algorithms. Customization Allows for modifications and extensions to meet specific needs. Open Source It's an open-source framework, allowing for community contributions and collaboration. Documentation Provides comprehensive documentation and tutorials to guide users.

15. Conclusion and Future Directions Federated learning is a transformative technology with the potential to revolutionize machine learning while addressing data privacy concerns. As research and development continue, we can expect to see even more innovative applications and advancements in this exciting field. Conclusion

16. Future Directions Advancements in Federated Learning Improved algorithms for better model aggregation Enhanced security measures (e.g., differential privacy) Integration with Other Technologies Combining with edge computing and blockchain Broader Adoption Wider use in various medical fields