# Federated Learning Versus Classical Machine Learning: A Convergence Comparison

**Authors:** Muhammad Asad, Ahmed Moustafa, Takayuki Ito  
**arXiv:** [2107.10976](https://arxiv.org/abs/2107.10976)  
**Date:** 2021-07-22  
**Summary page:** https://www.summarizepaper.com/en/paper/2107.10976v1/

## Key points

- Federated learning is a promising approach to address privacy concerns in machine learning models
- Traditional centralized data training poses significant privacy threats
- Federated learning allows participants to train local models on their own data without compromising privacy
- Research gaps exist in federated learning, including participant heterogeneity, clustering techniques, dynamic participant-cluster associations, and hierarchical architectures
- Challenges in machine-federated learning include data isolation and enhancing data safety and privacy
- Various methods have been proposed to improve the security of federated learning systems such as differential privacy mechanisms and encryption techniques
- Federated learning achieves higher convergence within limited communication rounds while maintaining participants' anonymity compared to classical machine learning
- Researchers have demonstrated the effectiveness of federated learning in preserving privacy while achieving comparable performance to traditional centralized training methods using publicly available datasets like logistic-regression-MNIST and image-classification-CIFAR-10 datasets
- Further research is needed to explore advanced techniques for enhancing the security and efficiency of federated learning systems

## Summary

Federated learning has emerged as a promising approach to address privacy concerns in machine learning models. Traditional centralized data training poses significant privacy threats when sensitive information is shared with a central server. However, federated learning allows participants to collaboratively train local models on their own data without compromising their privacy. Despite its potential benefits, federated learning has not been widely implemented yet and research gaps still exist. These include participant heterogeneity, clustering techniques, dynamic participant-cluster associations, and hierarchical architectures. Existing literature primarily focuses on privacy-preserving algorithms for both centralized and distributed learning. Challenges in machine-federated learning include data isolation and techniques for enhancing data safety and privacy. Researchers have proposed various methods to improve the security of federated learning systems such as differential privacy mechanisms and encryption techniques. Moreover, the comparison between classical machine learning and federated learning reveals that the latter achieves higher convergence within limited communication rounds while maintaining participants' anonymity. By conducting convergence comparisons on publicly available datasets like logistic-regression-MNIST and image-classification-CIFAR-10 datasets, researchers have demonstrated the effectiveness of federated learning in preserving privacy while achieving comparable performance to traditional centralized training methods. In conclusion, further research is needed to explore advanced techniques for enhancing the security and efficiency of federated learning systems. By addressing these challenges and refining existing methodologies, federated learning can be more widely adopted across various applications to ensure data privacy protection while maximizing model performance.