Abstract: Systems and methods are provided for training a machine learned model on a large number of devices, each device acquiring a local set of training data without sharing data sets across devices. The devices train the model on the respective device's set of training data. The devices communicate a parameter vector from the trained model asynchronously with a parameter server. The parameter server updates a master parameter vector and transmits the master parameter vector to the respective device.

Abstract: Systems and methods are provided for managing machine learning processes within distributed and heterogeneous environments. The distributed and heterogeneous environments may include different types of devices that include different specifications, security, and privacy concerns. The devices participate in complex machine learning tasks while maintaining both privacy and autonomy. The systems and methods manage the lifecycle of how machine learning workloads are distributed.

Abstract: Systems and methods are provided for training a model on a large number of devices where, for example, each device acquires a local set of training data without sharing data sets across the devices. The devices train the model on the respective device's set of training data. The devices communicate a parameter vector from the trained model asynchronously with a parameter server. The parameter server updates a master parameter vector and transmits the master parameter vector to the respective device. The update rate of the devices is decoupled from the size of the data that is available to the devices and the computational power of the devices by over or under sampling the local training data.

Abstract: Systems and methods are provided for training a machine learned model on a large number of devices, each device acquiring a local set of training data without sharing data sets across devices. The devices train the model on the respective device's set of training data. The devices communicate a parameter vector from the trained model asynchronously with a parameter server. The parameter server updates a master parameter vector and transmits the master parameter vector to the respective device.