  The longstanding goals of federated learning (FL) require rigorous privacy
guarantees and low communication overhead while holding a relatively high model
accuracy. However, simultaneously achieving all the goals is extremely
challenging. In this paper, we propose a novel framework called hierarchical
federated learning (H-FL) to tackle this challenge. Considering the degradation
of the model performance due to the statistic heterogeneity of the training
data, we devise a runtime distribution reconstruction strategy, which
reallocates the clients appropriately and utilizes mediators to rearrange the
local training of the clients. In addition, we design a compression-correction
mechanism incorporated into H-FL to reduce the communication overhead while not
sacrificing the model performance. To further provide privacy guarantees, we
introduce differential privacy while performing local training, which injects
moderate amount of noise into only part of the complete model. Experimental
results show that our H-FL framework achieves the state-of-art performance on
different datasets for the real-world image recognition tasks.
