Title: Exploit Unlabeled Data on the Server! Federated Learning via Uncertainty-aware Ensemble Distillation and Self-Supervision. Abstract: Federated Learning (FL) is a distributed machine learning paradigm that involves the cooperation of multiple clients to train a server model. In practice, it is hard to assume that each client possesses large-scale data or many clients are always available to participate in FL for the same round, which may lead to data deficiency. This deficiency degrades the entire learning process. To resolve this challenge, we propose a Federated learning with entropy-weighted ensemble Distillation and Self-supervised learning (FedDS). FedDS reliably deals with situations where not only the amount of data per client but also the number of clients is scarce. This advantage is achieved by leveraging the prevalent unlabeled data in the server. We demonstrate the effectiveness of FedDS on classification tasks for CIFAR-10/100 and PathMNIST. In CIFAR-10, our method shows the improvement over FedAVG by 12.54% in data deficient regime, and by 17.16% and 23.56% in more challenging scenarios of noisy label or Byzantine client cases, respectively.