An apparatus (100; 410) comprising a neural network, wherein the neural network comprises a classifier for objects in images and the apparatus is configured to train at least one object class of a plurality of object classes, the apparatus comprising means for receiving parameters for the neural network from a parameter server; means for updating the parameters of the neural network with the received parameters to obtain a current state of the neural network; means for obtaining image data comprising objects relating to at least said one object class; means for training the current state of the neural network, based on the image data, for the at least one object class; means for computing first updated parameters based on the training of the current state of the neural network; means for updating the parameters of the current state of the neural network with the first updated parameters to obtain a first updated state of the neural network; means for training the first updated state of the neural network for one or more classes of the plurality of object classes other than the at least one object class using image data similar to the image data relating to the at least one object class; means for computing second updated parameters based on the training of the first updated state of the neural network; means for updating the first updated parameters with the second updated parameters to obtain a second updated state of the neural network; means for comparing performance of the current state of the neural network and the second updated state of the neural network; means for modifying the first updated parameters when the performance of the second updated state of the neural network in classifying objects for the at least one object class is lower than the performance of the current state of the neural network; and means for transmitting the first modified parameters to a parameter server or to one or more devices being involved in a federated training of a neural network when the performance of the second updated state of the neural network is higher than the performance of the current state of the neural network. means for receiving parameters for the neural network from a parameter server; means for updating the parameters of the neural network with the received parameters to obtain a current state of the neural network; means for obtaining image data comprising objects relating to at least said one object class; means for training the current state of the neural network, based on the image data, for the at least one object class; means for computing first updated parameters based on the training of the current state of the neural network; means for updating the parameters of the current state of the neural network with the first updated parameters to obtain a first updated state of the neural network; means for training the first updated state of the neural network for one or more classes of the plurality of object classes other than the at least one object class using image data similar to the image data relating to the at least one object class; means for computing second updated parameters based on the training of the first updated state of the neural network; means for updating the first updated parameters with the second updated parameters to obtain a second updated state of the neural network; means for comparing performance of the current state of the neural network and the second updated state of the neural network; means for modifying the first updated parameters when the performance of the second updated state of the neural network in classifying objects for the at least one object class is lower than the performance of the current state of the neural network; and means for transmitting the first modified parameters to a parameter server or to one or more devices being involved in a federated training of a neural network when the performance of the second updated state of the neural network is higher than the performance of the current state of the neural network.