Federated learning is a cutting-edge machine learning technique that allows multiple decentralized devices or servers to collaboratively train a model while keeping data localized. By ensuring that raw data remains on the device and only model updates are shared with a central server, federated learning significantly enhances privacy and security. This approach is particularly advantageous for applications in sensitive fields such as healthcare, finance, and mobile applications, where protecting user data is crucial. By enabling collaborative training without compromising data confidentiality, federated learning fosters trust and compliance in environments that prioritize data privacy.
