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.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A distributed machine learning system comprising: a plurality of private data servers, each private data server having access to local private data and having at least one modeling engine, wherein the plurality of private data servers are communicatively coupled, via a network, to at least one non-private computing device; and wherein each private data server upon execution by at least one processor software instructions stored in a non-transitory computer readable memory causes its at least one modeling engine to: receive model instructions to create a trained actual model from at least some of the local private data and according to an implementation of a machine learning algorithm; create the trained actual model according to the model instructions and as a function of the at least some of the local private data by training the implementation of the machine learning algorithm on the local private data, the trained actual model comprising trained actual model parameters; generate a plurality of private data distributions from the local private data where the private data distributions represent the local private data in aggregate used to create the trained actual model; generate a set of proxy data according to the plurality of private data distributions; create a trained proxy model from the set of proxy data by training the type of machine learning model on the set of proxy data, the trained proxy model comprising proxy model parameters; calculate a model similarity score as a function of the proxy model parameters and the trained actual model parameters; and transmit the set of proxy data, over the network, to the at least one non-private computing device as a function of the model similarity score. 2. The system of claim 1, wherein the local private data comprises local private healthcare data. 3. The system of claim 2, wherein the local private healthcare data includes patient-specific data. 4. The system of claim 1, wherein the local private data includes at least one of the following types of data: genomic data, whole genome sequence data, whole exosome sequence data, proteomic data, proteomic pathway data, k-mer data, neoepitope data, RNA data, allergy information, encounter data, treatment data, outcome data, appointment data, order data, billing code data, diagnosis code data, results data, treatment response data, tumor response data, demographic data, medication data, vital sign data, payor data, drug study data, drug response data, longitudinal study data, biometric data, financial data, proprietary data, electronic medical record data, research data, human capital data, performance data, analysis results data, and event data. 5. The system of claim 1, wherein the network comprises at least one of the following types of networks: a wireless network, a packet switched network, the Internet, an intranet, a virtual private network, a cellular network, an ad hoc network, and a peer-to-peer network. 6. The system of claim 1, wherein the at least one non-private computing device is a different one of the plurality of private data servers lacking authorization to the local private data on which the trained actual model was created. 7. The system of claim 1, wherein the at least one non-private computing device includes a global model server. 8. The system of claim 8, wherein the global model server is configured to aggregate sets of proxy data from at least two of the plurality of private data servers and is configured to train a global model on the sets of proxy data. 9. The system of claim 1, wherein each private data server is commutatively coupled with a local storage system that stores the local private data. 10. The system of claim 9, wherein the local storage system includes at least one of the following: a RAID system, a file server, a network accessible storage device, a storage area network device, a local computer readable memory, a hard disk drive, an optical storage device, a tape drive, a tape library, and a solid state disk. 11. The system of claim 9, wherein the local storage system includes at least one of the following: a local database, a BAM server, a SAM server, a GAR server, BAMBAM server, and a clinical operating system server. 12. The system of claim 1, wherein the model instructions comprises at least one of the following: a local command, a remote command, an executable file, a protocol command, and a selected command. 13. The system of claim 1, wherein a distribution of the plurality private data distributions adheres to at least one of the following types of distributions: a Gaussian distribution, a Poisson distribution, a Bernoulli distribution, a Rademacher distribution, a discrete distribution, a binomial distribution, a zeta distribution, a Gamma distribution, a beta distribution, and a histogram distribution. 14. The system of claim 1, wherein the plurality of private data distributions are based on eigenvalues derived from the trained actual model parameters and the private local data. 15. The system of claim 1, wherein the set of proxy data includes combinations of eigenvectors derived from the trained actual model parameters and the local private data. 16. The system of claim 15, wherein the proxy data comprises linear combinations of the eigenvectors. 17. The system of claim 15, wherein the eigenvectors include at least one of the following: an eigenpatient, an eigenprofile, an eigendrug, an eigenhealth record, an eigengenome, an eigenproteome, an eigenRNA profile, and an eigenpathway. 18. The system of claim 1, wherein the trained actual model is based on an implementation of at least one of the following types of machine learning algorithms: a classification algorithm, a neural network algorithm, a regression algorithm, a decision tree algorithm, a clustering algorithm, a genetic algorithm, a supervised learning algorithm, a semi-supervised learning algorithm, an unsupervised learning algorithm, and a deep learning algorithm. 19. The system of claim 1, wherein the trained actual model is based on an implementation of at least one of the following machine learning algorithms: a support vector machine, a nearest neighbor algorithm, a random forest, a ridge regression, a Lasso algorithm, a k-means clustering algorithm, a spectral clustering algorithm, a mean shift clustering algorithm, a non-negative matrix factorization algorithm, an elastic net algorithm, a Bayesian classifier algorithm, a RANSAC algorithm, and an orthogonal matching pursuit algorithm. 20. The system of claim 1, wherein the model instructions include instructions to create the trained actual model from a base-line model created external to the private data server. 21. The system of claim 20, wherein the base-line model comprises a global trained actual model. 22. The system of claim 21, wherein the global trained actual model is trained, at least in part, on sets of proxy data from at least two of the plurality of private data servers. 23. The system of claim 1, wherein the similarity score is determined based on a cross validation of the proxy model. 24. The system of claim 23, wherein the cross validation includes an internal cross validation on a portion of the proxy data. 25. The system of claim 23, wherein the cross validation includes an internal cross validation of the local private data. 26. The system of claim 23, wherein the cross validation includes an external cross validation by a different one of the plurality of private data servers on its local private data. 27. The system of claim 1, wherein the similarity score comprises a difference between an accuracy measure of the proxy model and an accuracy measure of the trained actual model. 28. The system of claim 1, wherein the similarity score comprises a metric distance calculated from the trained actual model parameters and the proxy model parameters. 29. The system of claim 1, wherein the proxy data is transmitted when the function of the model similarity score satisfies at least one transmission criterion. 30. The system of claim 29, wherein the at least one transmission criterion include at least one of the following conditions relating to the similarity score: a threshold condition, a multi-valued condition, a change in value condition, a trend condition, a human command condition, an external request condition, and a time condition. 31. The system of claim 1, wherein the modeling engine is further configured to update the trained actual model on new local private data. 32. A computing device implemented method of distributed machine learning: receiving, by a private data server, model instructions to create a trained actual model from at least some of local private data local to the private data server and according to an implementation of a machine learning algorithm; creating, by a machine learning engine, the trained actual model according to the model instructions and as a function of the at least some of the local private data by training the implementation of the machine learning algorithm on the local private data, the trained actual model comprising trained actual model parameters; generating, by the machine learning engine, a plurality of private data distributions from the local private data where the private data distributions represent the local private data in aggregate used to create the trained actual model; identifying, by the machine learning engine, salient private data features from the private data distribution wherein the salient private data features allow for replication of the plurality of proxy data distributions; and transmitting, by the machine learning engine, the salient private data features over a network to a non-private computing device. 33. The method of claim 32, wherein the salient private data features includes a set of proxy data. 34. The method of claim 32, further comprising generating a set of proxy data according to at least one of the following: the plurality of private data distributions, and the salient private data features. 35. The method of claim 34, further comprising creating a trained proxy model from the set of proxy data by training the type of the implementation of the machine learning algorithm on the set of proxy data, the trained proxy model comprising proxy model parameters. 36. The method of claim 35, further comprising calculating a model similarity score of the trained proxy model as a function of the proxy model parameters and the trained actual model parameters. 37. The method of claim 36, further comprising aggregating the set of proxy data into an aggregated global model based on the model similarity score. 38. A computer implemented method of generating proxy data using a private data server configured to access local private data, and including at least one modeling engine configured to: create, from the private data, a trained actual model using a machine learning algorithm; generate a plurality of private data distributions from at least some of the local private data, wherein the private data distributions represent the local private data in aggregate; generate a set of proxy data based on the plurality of private data distributions; and create, from the set of proxy data, a trained proxy model using the machine learning algorithm.