Federated learning is a machine learning approach in which a model is trained across multiple devices or organizations without centralizing the raw data. It can support privacy and data governance goals by sharing model updates rather than local datasets.
A machine learning technique that allows for training models on multiple devices with decentralized data, thus helping preserve the privacy of individual users and their data.
A distributed machine learning approach that trains machine learning models using decentralized examples residing on devices such as smartphones. In federated learning, a subset of devices downloads the current model from a central coordinating server. The devices use the examples stored on the devices to make improvements to the model. The devices then upload the model improvements (but not the training examples) to the coordinating server, where they are aggregated with other updates to yield an improved global model. After the aggregation, the model updates computed by devices are no longer needed, and can be discarded. Since the training examples are never uploaded, federated learning follows the privacy principles of focused data collection and data minimization. See the Federated Learning comic (yes, a comic) for more details.