A computer-implemented method for privacy-preserving distributed training of a global machine learning model - referred to as global model - on distributed datasets (DS1 to DSn), wherein a plurality of data providers (DP1 to DPn) are communicatively coupled such that each data provider can exchange information with any other data provider, each data provider having: a respective local machine learning model (LM1 to LMn) - referred to as local model - and a respective local training dataset (DS1 to DSn) for training the local model using an iterative training algorithm, and a portion of a cryptographic distributed secret key (SK1 to SKn) and a corresponding collective cryptographic public key of a multiparty fully homomorphic encryption scheme, with the local model being encrypted with the collective public key, wherein the cryptographic keys (SK1 to SKn, CPK) are collectively initialized by the plurality of data providers (DS1 to DSn), and the local models (LM1 to LMn) and local training datasets (DS1 to DSn) of all data provider systems adhere to the same structure and data formats and use the same training parameters, and the plurality of all local training datasets represents a global training dataset for the global model, and the global model also being encrypted with the collective public key, the method comprising: for a predefined maximum number of global iterations: each data provider training its local model using the respective local training dataset by executing a predefined number of local iterations of an iterative training algorithm for its local model under homomorphic encryption, and combining the updated local model (LM1') with the current encrypted global model into a current local model (LM1c), and providing the current local model (LM1c) to at least a subset of the data providers (DP2, DP3); homomorphically combining at least a subset of the current local models by at least one of the subset of data providers into a combined model; and updating, by the at least one data provider, the current global model based on the combined model; providing, by the at least one data provider, the updated global model (GM') to at least a subset of the other data providers of the plurality of data providers. a respective local machine learning model (LM1 to LMn) - referred to as local model - and a respective local training dataset (DS1 to DSn) for training the local model using an iterative training algorithm, and a portion of a cryptographic distributed secret key (SK1 to SKn) and a corresponding collective cryptographic public key of a multiparty fully homomorphic encryption scheme, with the local model being encrypted with the collective public key, for a predefined maximum number of global iterations: each data provider training its local model using the respective local training dataset by executing a predefined number of local iterations of an iterative training algorithm for its local model under homomorphic encryption, and combining the updated local model (LM1') with the current encrypted global model into a current local model (LM1c), and providing the current local model (LM1c) to at least a subset of the data providers (DP2, DP3); homomorphically combining at least a subset of the current local models by at least one of the subset of data providers into a combined model; and updating, by the at least one data provider, the current global model based on the combined model; each data provider training its local model using the respective local training dataset by executing a predefined number of local iterations of an iterative training algorithm for its local model under homomorphic encryption, and combining the updated local model (LM1') with the current encrypted global model into a current local model (LM1c), and providing the current local model (LM1c) to at least a subset of the data providers (DP2, DP3); homomorphically combining at least a subset of the current local models by at least one of the subset of data providers into a combined model; and updating, by the at least one data provider, the current global model based on the combined model; providing, by the at least one data provider, the updated global model (GM') to at least a subset of the other data providers of the plurality of data providers.