Method and apparatus for federated training

An apparatus (120, 320, 520) for federated training, the apparatus comprising at least one processing core, at least one non-transitory memory including computer program code, the at least one memory and the computer program code being configured to, with the at least one processing core, to configure the apparatus for:

• Transmitting a first implementation (22, 42) of a data-processing model to a first distributed trainer (130, 330, 530), wherein the first implementation of the data-processing model comprises a first hidden part (221, 421) and a first open part (222, 422), wherein parameters of the first hidden part (221, 421) are not accessible to the first distributed trainer, wherein the first distributed trainer has access to a first dataset embedded in a first remote device,
• Transmitting a second implementation of the data-processing model to a second distributed trainer (130, 330, 530), wherein the second implementation (23, 43) of the data-processing model comprises a second hidden part (231, 431) and a second open part (232, 432), wherein parameters of the second hidden part (231;
• 431 are not accessible to the second distributed trainer, wherein the second distributed trainer has access to a second dataset embedded in a second remote device,
• Receiving a first training gradient (110, 310) from the first distributed trainer (130, 330, 530) and a second training gradient (110, 310) from the second distributed trainer, wherein the first gradient is computed using the first implementation (22, 42) of the data-processing model and the first dataset, wherein the first gradient relates to the first open part (222, 422) of the first implementation (22, 42) of the data-processing model, wherein the second gradient is computed using the second implementation (23, 43) of the data-processing model and the second dataset, wherein the second gradient relates to the second open part (232, 432) of the second implementation (23, 43) of the data-processing model,
• Updating the data-processing model using the first gradient (110, 310) and the second gradient,
• Associating a first trust score to the first distributed trainer (130, 330, 530) and a second trust score to the second distributed trainer (130, 330, 530),
• Determining the first hidden part (221, 421) and the first open part (222, 422) as a function of the first trust score and the second hidden part (231, 431) and the second open part (232, 432) as a function of the second trust score,
• Wherein in response to the first trust score being greater than the second trust score, a number of parameters in the second hidden part (231, 431) is larger than a number of parameters in the first hidden part (221, 421).