A computer-implemented method of operation of a server for cascaded federated machine learning, the method comprising: for a training epoch: receiving (1002-1 through 1 002-N D), from each client device of a plurality of client devices (304-1 through 304-N D): a local machine learning, ML, model for estimating one or more first parameters as trained at the client device for the training epoch; and an estimated value of each of the one or more first parameters output by the local ML model at the client device for the training epoch; aggregating the local ML models (316-1 through 31 6-N D) received from the plurality of client devices (304-1 through 304-N D) to provide a global ML model for estimating the one or more first parameters; training a network ML model based on: the estimated values of each of the one or more parameters output by the local ML models for the training epoch; and global data available at the server; wherein the network ML model comprises a neural network for estimating one or more second parameters; and providing (1008-1 through 1 008-N D), to each client device of the plurality of client devices (304-1 through 304-N D): the global ML model; and feedback information related to one of a plurality of hidden neural network layers of the neural network comprised in the network ML model for training the local ML models at the client device; and repeating the receiving, aggregating, training, and providing for one or more additional training epochs. for a training epoch: receiving (1002-1 through 1 002-N D), from each client device of a plurality of client devices (304-1 through 304-N D): a local machine learning, ML, model for estimating one or more first parameters as trained at the client device for the training epoch; and an estimated value of each of the one or more first parameters output by the local ML model at the client device for the training epoch; aggregating the local ML models (316-1 through 31 6-N D) received from the plurality of client devices (304-1 through 304-N D) to provide a global ML model for estimating the one or more first parameters; training a network ML model based on: the estimated values of each of the one or more parameters output by the local ML models for the training epoch; and global data available at the server; wherein the network ML model comprises a neural network for estimating one or more second parameters; and providing (1008-1 through 1 008-N D), to each client device of the plurality of client devices (304-1 through 304-N D): the global ML model; and feedback information related to one of a plurality of hidden neural network layers of the neural network comprised in the network ML model for training the local ML models at the client device; and receiving (1002-1 through 1 002-N D), from each client device of a plurality of client devices (304-1 through 304-N D): a local machine learning, ML, model for estimating one or more first parameters as trained at the client device for the training epoch; and an estimated value of each of the one or more first parameters output by the local ML model at the client device for the training epoch; a local machine learning, ML, model for estimating one or more first parameters as trained at the client device for the training epoch; and an estimated value of each of the one or more first parameters output by the local ML model at the client device for the training epoch; aggregating the local ML models (316-1 through 31 6-N D) received from the plurality of client devices (304-1 through 304-N D) to provide a global ML model for estimating the one or more first parameters; training a network ML model based on: the estimated values of each of the one or more parameters output by the local ML models for the training epoch; and global data available at the server; wherein the network ML model comprises a neural network for estimating one or more second parameters; and the estimated values of each of the one or more parameters output by the local ML models for the training epoch; and global data available at the server; wherein the network ML model comprises a neural network for estimating one or more second parameters; and providing (1008-1 through 1 008-N D), to each client device of the plurality of client devices (304-1 through 304-N D): the global ML model; and feedback information related to one of a plurality of hidden neural network layers of the neural network comprised in the network ML model for training the local ML models at the client device; and the global ML model; and feedback information related to one of a plurality of hidden neural network layers of the neural network comprised in the network ML model for training the local ML models at the client device; and repeating the receiving, aggregating, training, and providing for one or more additional training epochs.