Distributed machine learning system, apparatus, and method

Abstract

A distributed online machine learning system is presented. Contemplated systems include many private data servers, each having local private data. Researchers can request that relevant private data servers train implementations of machine learning algorithms on their local private data without requiring de-identification of the private data or without exposing the private data to unauthorized computing systems. The private data servers also generate synthetic or proxy data according to the data distributions of the actual data. The servers then use the proxy data to train proxy models. When the proxy models are sufficiently similar to the trained actual models, the proxy data, proxy model parameters, or other learned knowledge can be transmitted to one or more non-private computing devices. The learned knowledge from many private data servers can then be aggregated into one or more trained global models without exposing private data.

Description

[0001] Cross References to Related Applications

[0002] This application claims priority under Szeto's U.S. Provisional Patent Application Serial No. 62 / 363,697, filed July 18, 2016, entitled "Distributed Machine Learning System, Apparatus, and Method," under Title 35, U.S. Act, Section 119, which The content is hereby incorporated by reference in its entirety. technical field

[0003] The field of the invention is distributed machine learning techniques. Background technique

[0004] The Background Description includes information useful in understanding the subject matter of the invention. It is not an admission that any information presented herein is prior art or is relevant to presently claimed subject matter, or that any publication specifically or implicitly cited is prior art.

[0005] With the recent growth of highly accessible and cost-effective machine learning platforms (e.g., Google AI including TensorFlow, Amazon Machine Learning, Microsoft Azure Machine Lea...

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