Abstract: We introduce On-Demand Federated Learning (On-Demand FL), which enables on-demand federated learning of a deep model for an arbitrary target data distribution of interest by making the best use of the heterogeneity (non-IID-ness) of local client data, unlike existing approaches trying to circumvent the non-IID nature of federated learning. On-Demand FL composes a dataset of the target distribution, which we call the composite dataset, from a selected subset of local clients whose aggregate distribution is expected to emulate the target distribution as a whole. As the composite dataset consists of a precise yet diverse subset of clients reflecting the target distribution, the on-demand model trained with exactly enough selected clients becomes able to improve the model performance on the target distribution compared when trained with off-target and/or unknown distributions while reducing the number of participating clients and federating rounds. We model the target data distribution in terms of class and estimate the class distribution of each local client from the weight gradient of its local model. Our experiment results show that On-Demand FL achieves up to 5\% higher classification accuracy on various target distributions just involving 9${\times}$ fewer clients with FashionMNIST, CIFAR-10, and CIFAR-100.