Abstract: The current document is directed to reinforcement-learning-based management-system agents that control distributed applications and the infrastructure environments in which they run. Management-system agents are initially trained in simulated environments and specialized training environments before being deployed to live, target distributed computer systems where they operate in a controller mode in which they do not explore the control-state space or attempt to learn better policies and value functions, but instead produce traces that are collected and stored for subsequent use. Each deployed management-system agent is associated with a twin training agent that uses the collected traces produced by the deployed management-system agent for optimizing its policy and value functions. When the optimized policy is determined to be more robust, stable, and effective than the policy of the corresponding deployed management-system agent, the optimized policy is transferred to the deployed management-system agent.

Abstract: A method includes deploying a neural network (NN) model on an electronic device. The NN model is generated by training a first NN architecture on a first dataset. A first function defines a first layer of the first NN architecture. The first function is constructed based on approximating a second function applied by a second layer of a second NN architecture. Retraining of the NN model is enabled on the electronic device using a second data set.

Abstract: Techniques for implementing efficient federated learning of deep neural networks (DNNs) using approximation layers are provided. In one set of embodiments, given a DNN M with k original layers {L1, . . . , Lk}, k approximation layers {L1?, . . . , Lk?} can be created that correspond (i.e., map) to the k original layers. Each approximation layer can have the same number of inputs and outputs as its corresponding original layer, but can be smaller in size (i.e., have fewer parameters). Then, at the time of training DNN M via federated learning, for each participating client c during a training round r, a parameter server can transmit, for i=1, k, either (1) the current parameter values for approximation layer Li? alone, or (2) the current parameter values for both original layer Li and approximation layer Li? to client c. In response, client c can train its local copy of DNN M in accordance with the received parameter values.

Abstract: A method includes deploying a neural network (NN) model on an electronic device. The NN model being generated by training a first NN architecture on a first dataset. A first function defines a first layer of the first NN architecture. The first function is constructed based on approximating a second function applied by a second layer of a second NN architecture. Retraining of the NN model is enabled on the electronic device using a second data set.