Large Language Models

Security: With federated learning, only encrypted model updates are shared with the central server, ensuring data security. Techniques like the Secure Aggregation Principle further enhance security by decrypting only the aggregated results, not the individual data.
Privacy: Unlike traditional approaches where data is sent to a central server for training, federated learning allows the training to happen directly on the edge device, reducing the risk of data breaches.
Access to Diverse Data: Federated learning provides access to data distributed across numerous devices, locations, and organizations. This approach enables training models on sensitive information, such as financial or healthcare data, while preserving security and privacy.

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Understanding Model Drift In Machine Learning

In this guide, we'll explore different types of model drift, including concept and data drift, and discuss how to detect and tackle these issues. We'll also share some practical strategies for continuous retraining, model versioning, and monitoring performance metrics to keep your machine-learning models effective over time.

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