Federated Learning is an advanced machine learning technique that allows multiple decentralized devices or servers to collaboratively train a shared model without sharing their raw data with a central server. Instead, each participant trains the model locally on its own data and only shares model updates (like gradients or weights), which are then aggregated centrally. 🔍 What Is Federated Learning? Goal: Train AI models on data distributed across many devices (smartphones, hospitals, IoT devices) while keeping the data private and secure. Key Benefit: Enhances privacy and data security by not transferring sensitive raw data out of its original location. Example: Predictive text models on smartphones improve from users’ typing habits without uploading all their texts. ⚙️ How Federated Learning Works (Simplified) Initialization: Central server sends the current global model to all participating devices. Local Training: Each device trains the model locally us...