Abstract: A method for distributed application distribution may include: (1) receiving, at a first decentralized marketplace instance in a distributed ledger network and from a first node of the plurality of nodes, the first node associated with a distributed application creator, a distributed application; (2) making available, by the first decentralized marketplace instance, the distributed application to decentralized marketplace instances, wherein the first node is configured to provide the distributed application to a second node, the second node associated with a distributed application collaborator; (3) receiving, at a second decentralized marketplace instance and from the second node, a modified version of the distributed application; and (4) making available, by the second decentralized marketplace instance, the modified version of the distributed application to the decentralized marketplace instances, wherein the second node is configured to provide the distributed application to a third node, the third node a

Abstract: Systems and methods for optimized payment selection and intelligent routing are disclosed. According to one embodiment, a method for optimized payment selection and intelligent routing may include: (1) receiving, by a payment selection and routing engine, obligation data for a payment from a payor; (2) identifying, by a machine learning engine, payment information from the obligation data; (3) retrieving, by the machine learning engine, client data, account data, and payment route data from the payment data; (4) retrieving, by the machine learning engine, machine learning rules for payments; (5) applying, by the machine learning engine, the machine learning rules to predict a payment route of a plurality of payment routes for the payment; and (6) routing, by the machine learning engine, the payment using the payment route.

Abstract: A method for privacy preserving machine learning model sharing may include a computer program for a first institution of a plurality of institutions in a distributed ledger network: receiving transaction data for a transaction; training a local machine learning model using the transaction data; submitting parameters for the local machine learning model to the distributed ledger network as a private transaction with a trusted entity, wherein the trusted entity receives parameters for a plurality of local machine learning models from the distributed ledger network for the plurality of institutions in the distributed ledger network and aggregates the parameters into an aggregated machine learning model and submits the aggregated parameters to the distributed ledger network as one or more transactions; receiving, from the distributed ledger network, the aggregated parameters for the aggregated machine learning model; and updating the local machine learning model with the aggregated parameters.

Abstract: Systems and methods for generating synthetic data using federated, collaborative, privacy preserving learning models are disclosed. In one embodiment, a method for generating synthetic data from real data for use in a federated learning network may include: (1) conducting, by a backend for a first institution of a plurality of institutions in a federated learning network, a transaction comprising transaction data; (2) generating, by the backend for the first institution, local synthetic data for the transaction data using a local synthetic data generating model; (3) sharing, by the backend for the first institution, the local synthetic data with a plurality of backends for other institutions; (4) receiving, by the backend for the first institution, global synthetic data from the plurality of backends for the other institutions; and (5) training, by the backend for the first institution, a local machine learning model with the global synthetic data.