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Privacy-Preserving ML_ Federated Learning for Data Security

Learn how Federated Learning enhances data security while preserving privacy. Enroll in a machine learning course in Canada to master this cutting-edge technology.

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Privacy-Preserving ML_ Federated Learning for Data Security

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  1. Privacy-Preserving ML Federated Learning for Data Security This presentation explores privacy-preserving machine learning, focusing on federated learning. We'll examine how it protects sensitive data in ML applications. This intro sets the stage for understanding its benefits and challenges.

  2. Defining Privacy-Preserving Machine Learning PPML PPML includes techniques that train ML models without directly accessing sensitive data. Key goals include data confidentiality, model security, and compliance with regulations like GDPR and CCPA. Key Goals: • Data Confidentiality • Model Security • Compliance Examples of PPML include Federated Learning, Differential Privacy, Homomorphic Encryption and Secure Multi-Party Computation.

  3. Introduction to Federated Learning FL Global model sent to devices Local training on each device Updates sent to central server Server aggregates updates Federated Learning trains models across multiple devices without exchanging data. It "brings the algorithm to the data." Google's Keyboard App improves next-word prediction models using FL.

  4. Benefits of Federated Learning Enhanced Data Privacy Reduced Costs Improved Generalization Data stays on devices or secure environments, enhancing privacy. Only model updates are transmitted, reducing costs. Training on diverse datasets improves model accuracy. It helps meet data regulations and enables secure collaboration.

  5. Challenges of Federated Learning Communication Costs Heterogeneity of Data Device Availability Communication costs can be high. Data heterogeneity impacts model performance. Device availability varies. Model updates are vulnerable to attacks. Incentivizing participation fairly can be difficult. Ensuring all hospitals are compensated fairly is key.

  6. Applications of Federated Learning FL is used in healthcare for diagnostics and finance for fraud detection. It also optimizes telecom networks and trains autonomous vehicles. Retail uses it for personalized recommendations. Credit unions train loan default prediction models.

  7. Techniques to Enhance Federated Learning Differential Privacy Secure Aggregation Adding noise to model updates Using encryption ensures the server cannot see individual updates, only protects data points. For the aggregate result. Applying a example, adding random noise to masking algorithm to encrypt a hospital's data during training updates before sending them can protect patient privacy. protects individual datasets. Model Poisoning Defenses Tracking update history and filtering anomalous update contributions to prevent poisoned contributions from untrusted sources.

  8. Conclusion: The Future of Federated Learning Privacy Protect sensitive data Utility Leverage power of ML Federated Learning ensures data utility while protecting privacy, making it essential for the future. With stricter regulations, it enables secure ML applications. Enrolling in a machine learning course in Canada equips professionals with the skills to implement this technology effectively.

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