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AssigneeInveniam Capital Partners, Inc.
InventorsPaul Snow, Brian Deery, Mahesh Paolini-Subramanya, Jason Nadeau
Estimating the term…
Data verification in federate learning is faster and simpler. As artificial intelligence grows in usage, data verification is needed to prove custody and/or control. Electronic data representing an original version of training data may be hashed to generate one or more digital signatures. The digital signatures may then be incorporated into one or more blockchains for historical documentation. Any auditor may then quickly verify and/or reproduce the training data using the digital signatures. For example, a current version of the training data may be hashed and compared to the digital signatures generated from the current version of the training data. If the digital signatures match, then the training data has not changed since its creation. However, if the digital signatures do not match, then the training data has changed since its creation. The auditor may thus flag the training data for additional investigation and scrutiny.
A method executed by a server modifying a federated learning model based on a plurality of local updates reported by a plurality of client devices, the method comprising:
A server modifying a federated learning model based on a plurality of local updates reported by a plurality of client devices, comprising:
A memory device storing instructions that when executed by a hardware processor perform operations, the operations comprising:
Dependencies are inferred from the claim text.