Federated Learning

Federated learning is a decentralized machine learning approach that enables the training of models across multiple devices or servers without centralizing the data. This method allows various participants, such as mobile devices or edge servers, to collaboratively train a shared model while keeping their individual datasets local. Only model updates, not the raw data, are sent to a central server for aggregation. This approach enhances privacy and security, as sensitive information remains on the user’s device. Federated learning is particularly beneficial in scenarios where data privacy is paramount and data distribution is highly heterogeneous. It finds applications in various fields, including healthcare and mobile applications, improving model accuracy and user experience without compromising data security.