# FedComm: Understanding Communication Protocols for Edge-based Federated Learning

**Authors:** Gary Cleland, Di Wu, Rehmat Ullah, Blesson Varghese  
**arXiv:** [2208.08764](https://arxiv.org/abs/2208.08764)  
**Date:** 2022-08-18  
**Summary page:** https://www.summarizepaper.com/en/paper/2208.08764v1/

## Key points

- The paper addresses the issue of communication overhead and its impact on federated learning (FL) systems.
- FedComm is a benchmarking methodology proposed by the authors to quantify the impact of various application layer protocols on FL performance.
- The study focuses on both optimized protocols (MQTT, AMQP, ZMTP) and non-optimized protocols (TCP, UDP).
- FedComm measures FL performance in terms of communication time and accuracy under varying stress conditions.
- TCP outperforms UDP as a non-optimized protocol in terms of accuracy and shorter communication times for 4G and Wi-Fi networks.
- Optimized protocols such as AMQP, MQTT, and ZMTP achieve a 2.5x reduction in communication time compared to TCP while maintaining accuracy.
- The results provide valuable insights for designing efficient FL systems.
- FedComm is available for download from GitHub.

## Summary

The paper "FedComm: Understanding Communication Protocols for Edge-based Federated Learning" addresses the issue of communication overhead and its impact on the performance of federated learning (FL) systems. FL is a machine learning technique that trains models on devices using locally generated data, without transferring raw data to a central server. The authors propose FedComm, a benchmarking methodology that quantifies the impact of various application layer protocols on FL performance. They highlight the limited understanding of how different communication protocols contribute to the efficiency of FL training and aim to fill this gap with their research. The study focuses on both optimized protocols like Message Queue Telemetry Transport (MQTT), Advanced Message Queuing Protocol (AMQP), and ZeroMQ Message Transport Protocol (ZMTP), as well as non-optimized protocols like TCP and UDP. FedComm measures the overall performance of FL in terms of communication time and accuracy under varying computational and network stress conditions, including packet loss rates. The experiments are conducted on a lab-based testbed to evaluate the performance of different protocols. The results show that TCP outperforms UDP as a non-optimized application layer protocol in terms of accuracy and shorter communication times for 4G and Wi-Fi networks. Additionally, optimized application layer protocols such as AMQP, MQTT, and ZMTP outperform non-optimized protocols in most network conditions, achieving a 2.5x reduction in communication time compared to TCP while maintaining accuracy. These findings provide valuable insights for designing efficient FL systems. In conclusion, this paper presents FedComm as a benchmarking methodology for understanding how different communication protocols impact the performance of edge-based federated learning systems. The experimental results demonstrate the superiority of optimized application layer protocols over non-optimized ones, shedding light on several open research issues in the field of FL. The authors make their benchmarking methodology FedComm available for download from GitHub.