Abstract: Systems and methods for scalable and flexible federated learning frameworks are disclosed. A method may include: (1) receiving, by a computer program executed by an electronic device and from a client, a project for federated learning using a training federation, the training federation comprising a plurality of clients; (2) generating, by the computer program, a configuration file that reflects a set-up for the training federation; (3) receiving, by the computer program, files necessary to build containers, wherein at least some of the files are customized by the client; (4) generating, by the computer program, containers comprising the configuration file and files necessary to build the containers; and (5) deploying, by the computer program, the containers to a client compute environment for the client as a client node, wherein the client node is configured to join the training federation as a server and/or a participant.

Abstract: Systems and methods for generating runtime predictions in distributed computer architectures are disclosed. According to one embodiment, a method for generating runtime predictions in a distributed computer architectures may include: (1) receiving, by a runtime prediction computer program executed by an electronic device, training data regarding completion of a plurality of risk compute jobs; (2) extracting, by the runtime prediction computer program, bucketing statistics and instrument level features from the data; (3) training, by the runtime prediction computer program, a machine learning model with the training data, the extracted bucketing statistics, and the instrument level features; (4) receiving, by the runtime prediction computer program, a plurality of jobs for a period of time; and (5) calculating, by the runtime prediction computer program, an instrument cost for each of the jobs using the machine learning model.

Abstract: Systems and methods for federated model validation and data verification are disclosed. A method may include: (1) receiving, by a local computer program executed by client system, a federated machine learning model from a federated model server; (2) testing, by the local computer program and using a policy service, the federated machine learning model for vulnerabilities to attacks; (3) accepting, by the local computer program, the federated machine learning model in response to the federated machine learning model passing the testing; (4) training, by the local computer program, the federated machine learning model using input data comprising local data and outputting training parameters; (5) identifying, by the local computer program using the policy service, accidental leakage and/or contamination by comparing the training parameters to the input data; and (6) providing, by the local computer program, the training parameters to the federated model server.

Abstract: Systems and methods for mixed precision machine learning with fully homomorphic encryption are disclosed. A method may include receiving data in a mixed precision format from a program or an application executed by the client electronic device; converting the data from the mixed precision format to an integer format; encrypting the data in the integer format using a fully homomorphic data encryption scheme; communicating the encrypted data in the integer format to a host electronic device, wherein the host electronic device is configured to process the encrypted data in the integer format and provide an encrypted result in the integer format to the client electronic device; decrypting the encrypted result in the integer format using the fully homomorphic data encryption scheme; converting the decrypted result in the integer format to the mixed precision format; and outputting the result in the mixed precision format to the program or the application.

Abstract: Systems and methods for generating synthetic data using a learned copula are disclosed.