Federated Learning

Federated Learning is a decentralized machine learning approach that enables multiple parties to collaboratively train a shared predictive model while keeping their respective data localized. Instead of transferring raw data to a central server, each participant trains the model on their own devices or servers, and only the model updates (like gradients) are shared. This process helps to maintain data privacy, enhance security, and reduce bandwidth usage since sensitive information remains on-site. Federated Learning is especially useful in scenarios where data is distributed across various locations or devices, such as in mobile applications or healthcare systems, where data privacy regulations are paramount. Through this approach, a global model can be improved iteratively by aggregating the updates received from individual participants.