asynchronous-machine-learning/Asynchronous-Differentially-Private-Learning-on-Distributed-Private-Data
We develop asynchronous differentially-private algorithms for collaboratively training machine-learning models on multiple private datasets. The asynchronous nature of the algorithms implies that a central learner interacts with the private data owners one-on-one whenever they are available for communication without needing to aggregate query responses to construct gradients of the entire fitness function.
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