Abstract: Privacy-preserving federated learning apparatuses, systems, computer program products, and methods are provided that generate an updated global model based on a set of client models while maintaining privacy regarding the data values embodying each client model and the updated global model. In this regard, masked client models are utilized, which cryptographically obfuscate data values embodying the client model while still enabling combination, or “aggregation,” of the masked client models to generate a masked updated global model. The masked updated global model similarly includes obfuscated data values embodying the updated global model, but may be unmasked to reveal the true values of the updated global model for use. Some embodiments utilize specific steps for communication between environments, systems, devices, and/or the like, to ensure the masked models can only be unmasked by intended entities.

Abstract: Privacy-preserving federated learning apparatuses, systems, computer program products, and methods are provided that generate an updated global model based on a set of client models while maintaining privacy regarding the data values embodying each client model and the updated global model. In this regard, masked client models are utilized, which cryptographically obfuscate data values embodying the client model while still enabling combination, or “aggregation,” of the masked client models to generate a masked updated global model. The masked updated global model similarly includes obfuscated data values embodying the updated global model, but may be unmasked to reveal the true values of the updated global model for use. Some embodiments utilize specific steps for communication between environments, systems, devices, and/or the like, to ensure the masked models can only be unmasked by intended entities.