Also known as collaborative learning, Federated learning is a machine learning technique where a model is trained across multiple decentralized devices or servers keeping local data sets without the need to exchange or share this data. It allows other groups to use data to train their systems without having to share that data overcoming challenges related to data sharing, data governance, data privacy, and data security. Applications are focused especially in defense, telecommunications, IoT, finance, and pharmaceuticals.
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