Abstract: A computer-implemented method and a distributed computer system for privacy-preserving distributed training of a global neural network model on distributed datasets. The system has data providers communicatively coupled with each having a respective local training dataset and a vector of output labels for training the global model. Further, it has a cryptographic distributed secret key and a corresponding collective cryptographic public key of a multiparty fully homomorphic encryption scheme, with the weights of the global model being encrypted with the collective public key. Each data provider computes and aggregates, for each layer of the global model, encrypted local gradients using the respective local training dataset and output labels, with forward pass and backpropagation using stochastic gradient descent.

Abstract: A system for privacy-preserving distributed training of a global model on distributed datasets has a plurality of data providers being communicatively coupled. Each data provider has a local model and a local training dataset for training the local model using an iterative training algorithm. Further it has a portion of a cryptographic distributed secret key and a corresponding collective cryptographic public key of a multiparty fully homomorphic encryption scheme. All models are encrypted with the collective public key. Each data provider trains its local model using the respective local training dataset, and combines the local model with the current global model into a current local model. A data provider homomorphically combines current local models into a combined model, and updates the current global model based on the combined model. The updated global model is provided to at least a subset of the other data providers.

Abstract: A computer-implemented method and a distributed computer system (100) for privacy-preserving distributed training of a global neural network model on distributed datasets (DS1 to DSn). The system has a plurality of data providers (DP1 to DPn) being communicatively coupled. Each data provider has a respective local training dataset (DS1 to DSn) and a vector of output labels (OL1 to OLn) for training the global model. Further, it has a portion of a cryptographic distributed secret key (SK1 to SKn) and a corresponding collective cryptographic public key (CPK) of a multiparty fully homomorphic encryption scheme, with the weights of the global model being encrypted with the collective public key. Each data provider (DP1) computes and aggregates, for each layer of the global model, encrypted local gradients (LG1) using the respective local training dataset (DS1) and output labels (OL1), with forward pass and backpropagation using stochastic gradient descent.

Abstract: A computer-implemented method and a distributed computer system (100) for privacy-preserving distributed training of a global model on distributed datasets (DS1 to DSn). The system has a plurality of data providers (DP1 to DPn) being communicatively coupled. Each data provider has a respective local model (LM1 to LMn) and a respective local training dataset (DS1 to DSn) for training the local model using an iterative training algorithm (IA). Further it has a portion of a cryptographic distributed secret key (SK1 to SKn) and a corresponding collective cryptographic public key (CPK) of a multiparty fully homomorphic encryption scheme, with the local and global model being encrypted with the collective public key. Each data provider (DP1) trains its local model (LM1) using the respective local training dataset (DS1) by executing gradient descent updates of its local model (LM1), and combining (1340) the updated local model (LM1?) with the current global model (GM) into a current local model (LM1c).