Hierarchical Federated Learning with Quantization: Convergence Analysis and System Design

Understand

Federated learning (FL) is a powerful distributed machine learning framework where a server aggregates models trained by different clients without accessing their private data.

• Hierarchical FL, with a client-edge-cloud aggregation hierarchy, can effectively leverage both the cloud server's access to many clients' data and the edge servers' closeness to the clients to achieve a high communication efficiency.
• Neural network quantization can further reduce the communication overhead during model uploading.
• To fully exploit the advantages of hierarchical FL, an accurate convergence analysis with respect to the key system parameters is needed.