H-FL: A Hierarchical Communication-Efficient and Privacy-Protected Architecture for Federated Learning

Understand

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.