  Federated learning involves training machine learning models over devices or
data silos, such as edge processors or data warehouses, while keeping the data
local. Training in heterogeneous and potentially massive networks introduces
bias into the system, which is originated from the non-IID data and the low
participation rate in reality. In this paper, we propose Elastic Federated
Learning (EFL), an unbiased algorithm to tackle the heterogeneity in the
system, which makes the most informative parameters less volatile during
training, and utilizes the incomplete local updates. It is an efficient and
effective algorithm that compresses both upstream and downstream
communications. Theoretically, the algorithm has convergence guarantee when
training on the non-IID data at the low participation rate. Empirical
experiments corroborate the competitive performance of EFL framework on the
robustness and the efficiency.
