Title: Sself: Robust Federated Learning against Stragglers and Adversaries. Abstract: While federated learning allows efficient model training with local data at edge devices, two major issues that need to be resolved are: slow devices known as stragglers and malicious attacks launched by adversaries. While the presence of both stragglers and adversaries raises serious concerns for the deployment of practical federated learning systems, no known schemes or known combinations of schemes, to our best knowledge, effectively address these two issues at the same time. In this work, we propose Sself, a semi-synchronous entropy and loss based filtering/averaging, to tackle both stragglers and adversaries simultaneously. The stragglers are handled by exploiting different staleness (arrival delay) information when combining locally updated models during periodic global aggregation. Various adversarial attacks are tackled by utilizing a small amount of public data collected at the server in each aggregation step, to first filter out the model-poisoned devices using computed entropies, and then perform weighted averaging based on the estimated losses to combat data poisoning and backdoor attacks. A theoretical convergence bound is established to provide insights on the convergence of Sself. Extensive experimental results show that Sself outperforms various combinations of existing methods aiming to handle stragglers/adversaries.