Many learning tasks, such as regression and classification, are usually framed that way [1]. When N 1, computing the gradient of the objective in(1) becomes a bottleneck, even if individual gradients θL(zi,θ) are cheap to evaluate. For a fixed computational budget, itisthustempting toreplace vanilla gradient descent bymore iterations but using anapproximate gradient, obtained using only afewdata points. Stochastic gradient descent (SGD; [2]) follows this template.

It has been repeatedly shown that deep neural networks (DNNs) can achieve human-or superhuman-levelperformances on various tasks [1,17,32].

Markov decision process (MDP) is a paradigm for modeling sequential decision making under uncertainty. From a modeling perspective, some parameters of MDPs are unknown and need to be estimated from data. In this paper, we consider MDPs where transition probability and cost parametersarenotknown.

In this paper, we consider the federated meta-learning problem of learning personalization strategies. Specifically, we consider meta-nets that induce the batch-norm and learning rate parameters for each client given local data statistics.