Federated Learning is a machine learning approach where a model is trained across multiple decentralized devices or servers holding data samples, without exchanging them. This technique stands out for its ability to learn from a vast network of devices, like smartphones or IoT devices, while keeping all the training data local.
Here's a breakdown of its key components and benefits:
1. Decentralized Training: Instead of relying on a central dataset, federated learning allows models to be trained on user devices. Each device trains an individual model based on its data.
2. Privacy Preservation: As data remains on the user's device and only model updates are sent to the server, this method significantly enhances privacy and security, making it ideal for sensitive data applications.
3. Efficient Use of Resources: It leverages the computational power of participating devices, reducing the need for powerful central servers.
4. Collaborative Learning: Devices share model updates, which are aggregated to improve a global model, ensuring continuous learning and adaptation.