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When user-generated data such as audio and video signals are used to train machine learning algorithms, users' privacy must be considered before the learned model is released. In this work, we present an open-source library for privacypreserving machine learning framework on smart devices. The library allows Android and iOS devices to collectively learn a common classifier/regression model from distributed data with differential privacy, using a variant of minibatch stochastic gradient descentdoi:10.1109/icassp.2017.7953387 dblp:conf/icassp/HammLX17 fatcat:dqkz7uuyyva47moppvqharste4