# Edge-Assisted Hierarchical Federated Learning with Non-IID Data

**Authors:** Lumin Liu, Jun Zhang, S. H. Song, Khaled B. Letaief  
**arXiv:** [1905.06641](https://arxiv.org/abs/1905.06641)  
**Date:** 2019-05-16  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/1905.06641v1/

## Key points

- Paper title: "Edge-Assisted Hierarchical Federated Learning with Non-IID Data"
- Authors: Lumin Liu, Jun Zhang, S. H. Song, Khaled B. Letaief
- Communication latency and strain on network infrastructure
- Proposed solution: Hierarchical FL architecture with mobile edge computing platform
- Reduces communication rounds between users and cloud
- Local aggregations at edge servers before global aggregation at cloud
- Optimizes model performance while minimizing communication overhead
- Suitable for non-IID data distribution among devices
- Convergence of HierFAVG for convex and non-convex objective functions with non-IID user data demonstrated
- Edge-Assisted Hierarchical Federated Learning enhances FL efficiency and effectiveness in real-world applications involving diverse distributed datasets across mobile and IoT devices

## Summary

The paper "Edge-Assisted Hierarchical Federated Learning with Non-IID Data" by Lumin Liu, Jun Zhang, S. H. Song, and Khaled B. Letaief addresses the challenges and solutions in implementing Federated Learning (FL) for leveraging distributed private data on mobile phones and IoT devices. FL involves collaboratively training a shared machine learning model with the assistance of a cloud server. However, this process can lead to significant communication latency and strain on network infrastructure. To address these issues, the authors propose a hierarchical FL architecture that incorporates a mobile edge computing platform as an intermediary structure. This approach aims to reduce communication rounds between users and the cloud by introducing local aggregations at edge servers before performing a global aggregation at the cloud. The proposed Hierarchical Federated Averaging (HierFAVG) algorithm optimizes model performance while minimizing communication overhead in scenarios where data distribution among devices is non-identically and independently distributed (non-IID). The authors establish the convergence of HierFAVG for both convex and non-convex objective functions with non-IID user data, demonstrating its ability to achieve desired model performance with reduced communication requirements compared to traditional Federated Averaging algorithms. Through their research, Liu et al. showcase how Edge-Assisted Hierarchical Federated Learning offers a promising solution to enhance FL efficiency and effectiveness in real-world applications involving diverse distributed datasets across mobile and IoT devices.