Protecting assets of mutually distrustful entities during federated learning training on a remote device

Abstract

An apparatus to facilitate protecting assets of mutually distrustful entities during federated learning training on a remote device is disclosed. The apparatus includes a processor to a processor to: receive, at a trusted execution environment (TEE) hosted by a client platform, an encrypted machine learning (ML) model and a cryptographic message authentication code (MAC) from a model owner platform, wherein the encrypted ML model is encrypted by the model owner platform using homomorphic encryption (HE); verify integrity of the encrypted ML model using the cryptographic MAC and a TEE key established by the processor during remote attestation of the TEE with the model owner platform; perform, in the TEE, training of the encrypted ML model using HE computation on sensor data; and send, to the model owner platform, output of the training comprising updated model parameters of the encrypted ML model, where the output is homomorphically encrypted.