Federated learning is a distributed machine learning approach that allows training models on decentralized data sources.
Instead of aggregating data in a central server, federated learning brings the model to the data, allowing training to occur on edge servers.
Privacy is a key advantage of federated learning since data remains on the local devices.
Federated learning is particularly useful in scenarios where data cannot or should not be moved due to privacy concerns.
It enables training on sensitive data, such as personal health records, financial information,etc.
Federated learning can reduce the communication bandwidth requirements compared to traditional centralized approaches
Federated learning can leverage the computational power of edge devices, making it suitable for resource-constrained environments.
Federated learning has applications in various fields, including healthcare, finance, Internet of Things (IoT), and mobile devices.