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Privacy and Trust Redefined in Federated Machine Learning
2021
Machine Learning and Knowledge Extraction
A common privacy issue in traditional machine learning is that data needs to be disclosed for the training procedures. In situations with highly sensitive data such as healthcare records, accessing this information is challenging and often prohibited. Luckily, privacy-preserving technologies have been developed to overcome this hurdle by distributing the computation of the training and ensuring the data privacy to their owners. The distribution of the computation to multiple participating
doi:10.3390/make3020017
fatcat:zwci2c4rnbh5jil5p5cu5khubq