Federated learning is a distributed machine learning paradigm that enables multiple decentralized clients to collaboratively train a shared model without exchanging their local data. This research field investigates algorithms and systems for iterative model aggregation, secure communication, and privacy preservation, facilitating the development of robust models from sensitive, geographically dispersed datasets while maintaining data locality and minimizing data transfer.