Federated Learning

Federated Learning is a machine learning approach that enables multiple devices to collaboratively train a shared model while keeping their data localized. This method enhances privacy and security since the raw data never leaves the device; instead, only model updates are shared. Key characteristics include decentralized data processing, reduced bandwidth usage, and the ability to learn from diverse datasets without compromising user privacy. Common use cases include mobile device applications, healthcare data analysis, and any scenario where data privacy is paramount. By leveraging federated learning, organizations can improve model accuracy while adhering to data protection regulations.