Secure broker-mediated data analysis and prediction

A non-transitory, computer-readable medium with instructions stored thereon, wherein the instructions are executable by a processor to perform a method, comprising:

• receiving a plurality of datasets, wherein each dataset of the plurality of datasets is received from a respective client computing device of a plurality of client computing devices, wherein each dataset corresponds to a set of recorded values, and wherein each dataset comprises objects;
• determining a respective list of identifiers for each dataset and a composite list of identifiers comprising a combination of the lists of identifiers of each dataset of the plurality of datasets;
• determining a set of unique objects from among the plurality of datasets;
• selecting a subset of identifiers from the composite list of identifiers;
• determining a subset of the set of unique objects corresponding to each identifier in the subset of identifiers;
• computing a shared representation of the datasets based on the subset of the set of unique objects and a shared function having one or more shared parameters;
• determining a subset of objects for the respective dataset of each client computing device based on an intersection of the subset of identifiers with the list of identifiers for the respective dataset;
• determining a partial representation for the respective dataset of each client computing device based on the subset of objects for the respective dataset and the shared representation;
• acquiring one or more feedback values, wherein each of the one or more feedback values is determined based on a relation of predictions of the partial representation to the set of recorded values for the respective dataset;
• determining based on the subsets of objects and the one or more feedback values from the client computing devices, one or more aggregated feedback values;
• and
• updating the one or more shared parameters based on the one or more aggregated feedback values.

A memory with a model stored thereon, wherein the model is generated according to a method, comprising:

• receiving, by a managing computing device, a plurality of datasets, wherein each dataset of the plurality of datasets is received from a respective client computing device of a plurality of client computing devices, wherein each dataset corresponds to a set of recorded values, and wherein each dataset comprises objects;
• determining, by the managing computing device, a respective list of identifiers for each dataset and a composite list of identifiers comprising a combination of the lists of identifiers of each dataset of the plurality of datasets;
• determining, by the managing computing device, a set of unique objects from among the plurality of datasets;
• selecting, by the managing computing device, a subset of identifiers from the composite list of identifiers;
• determining, by the managing computing device, a subset of the set of unique objects corresponding to each identifier in the subset of identifiers;
• computing, by the managing computing device, a shared representation of the datasets based on the subset of the set of unique objects and a shared function having one or more shared parameters;
• determining, by the managing computing device, a subset of objects for the respective dataset of each client computing device based on an intersection of the subset of identifiers with the list of identifiers for the respective dataset;
• determining, by the managing computing device, a partial representation for the respective dataset of each client computing device based on the subset of objects for the respective dataset and the shared representation;
• acquiring, by the managing computing device, one or more feedback values, wherein each of the one or more feedback values is determined based on a relation of predictions of the partial representation to the set of recorded values for the respective dataset;
• determining, by the managing computing device, based on the subsets of objects and the one or more feedback values from the client computing devices, one or more aggregated feedback values;
• updating, by the managing computing device, the one or more shared parameters based on the one or more aggregated feedback values;
• and
• storing, by the managing computing device, the shared representation, the shared function, and the one or more shared parameters on the memory.

A method for machine learning, comprising:

• transmitting, by a first client computing device to a managing computing device, a first dataset corresponding to the first client computing device,
• wherein the first dataset is one of a plurality of datasets transmitted to the managing computing device by a plurality of client computing devices,
• wherein each dataset corresponds to a set of recorded values, and
• wherein each dataset comprises objects;
• receiving, by the first client computing device, a first subset of objects for the first dataset and a first partial representation for the first dataset, wherein the first subset of objects is a subset of the objects of the first dataset being part of forming a shared representation of the plurality of datasets, the shared representation being defined by a shared function having one or more shared parameters, and the partial representation for the first dataset is based on the first subset of objects and the shared representation;
• determining, by the first client computing device, a first set of predicted values corresponding to the first dataset, wherein the first set of predicted values is based on the first partial representation and a first individual function with one or more first individual parameters corresponding to the first dataset;
• determining, by the first client computing device, a first error for the first dataset based on a first individual loss function for the first dataset, the first set of predicted values corresponding to the first dataset, the first subset of objects, and non-empty entries in the set of recorded values corresponding to the first dataset;
• updating, by the first client computing device, the one or more first individual parameters for the first dataset;
• determining, by the first client computing device, one or more feedback values, wherein the one or more feedback values are used to determine a change in the first partial representation that corresponds to an improvement in the first set of predicted values;
• and
• transmitting, by the first client computing device to the managing computing device, the one or more feedback values,
• wherein the one or more feedback values are usable by the managing computing device along with subsets of objects from the plurality of client computing devices to determine one or more aggregated feedback values, and
• wherein the one or more aggregated feedback values are usable by the managing computing device to update the one or more shared parameters.