  Federated Learning (FL) is a novel distributed machine learning which allows
thousands of edge devices to train model locally without uploading data
concentrically to the server. But since real federated settings are
resource-constrained, FL is encountered with systems heterogeneity which causes
a lot of stragglers directly and then leads to significantly accuracy reduction
indirectly. To solve the problems caused by systems heterogeneity, we introduce
a novel self-adaptive federated framework FedSAE which adjusts the training
task of devices automatically and selects participants actively to alleviate
the performance degradation. In this work, we 1) propose FedSAE which leverages
the complete information of devices' historical training tasks to predict the
affordable training workloads for each device. In this way, FedSAE can estimate
the reliability of each device and self-adaptively adjust the amount of
training load per client in each round. 2) combine our framework with Active
Learning to self-adaptively select participants. Then the framework accelerates
the convergence of the global model. In our framework, the server evaluates
devices' value of training based on their training loss. Then the server
selects those clients with bigger value for the global model to reduce
communication overhead. The experimental result indicates that in a highly
heterogeneous system, FedSAE converges faster than FedAvg, the vanilla FL
framework. Furthermore, FedSAE outperforms than FedAvg on several federated
datasets - FedSAE improves test accuracy by 26.7% and reduces stragglers by
90.3% on average.
