Learning

A machine learning technique where multiple devices or servers collaboratively train a shared model without sharing raw data. Instead of sending data to a central server, only the model updates (gradients/parameters) are sent, keeping sensitive information local. Key Concepts Decentralized Training: Data stays on local devices (e.g., smartphones, IoT, edge devices). Model Aggregation: A central server collects and averages model updates to improve the global model. Privacy-Preserving: Minimizes risk of exposing personal or sensitive data. Communication Efficiency: Reduces the need for large-scale raw data transfer. Edge AI Integration: Often paired with edge computing for real-time AI. How Federated Learning Works [...]