Federated Learning Versus Classical Machine Learning: A Convergence Comparison

07/22/2021
by   Ahmed Moustafa, et al.
10

In the past few decades, machine learning has revolutionized data processing for large scale applications. Simultaneously, increasing privacy threats in trending applications led to the redesign of classical data training models. In particular, classical machine learning involves centralized data training, where the data is gathered, and the entire training process executes at the central server. Despite significant convergence, this training involves several privacy threats on participants' data when shared with the central cloud server. To this end, federated learning has achieved significant importance over distributed data training. In particular, the federated learning allows participants to collaboratively train the local models on local data without revealing their sensitive information to the central cloud server. In this paper, we perform a convergence comparison between classical machine learning and federated learning on two publicly available datasets, namely, logistic-regression-MNIST dataset and image-classification-CIFAR-10 dataset. The simulation results demonstrate that federated learning achieves higher convergence within limited communication rounds while maintaining participants' anonymity. We hope that this research will show the benefits and help federated learning to be implemented widely.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 5

page 6

page 7

03/22/2021

Server Averaging for Federated Learning

Federated learning allows distributed devices to collectively train a mo...
04/23/2020

Enhancing Privacy via Hierarchical Federated Learning

Federated learning suffers from several privacy-related issues that expo...
06/24/2021

Privacy Threats Analysis to Secure Federated Learning

Federated learning is emerging as a machine learning technique that trai...
03/23/2022

Efficient Fully Distributed Federated Learning with Adaptive Local Links

Nowadays, data-driven, machine and deep learning approaches have provide...
01/07/2021

Machine Learning on Cloud with Blockchain: A Secure, Verifiable and Fair Approach to Outsource the Linear Regression

Linear Regression (LR) is a classical machine learning algorithm which h...
12/15/2020

CosSGD: Nonlinear Quantization for Communication-efficient Federated Learning

Federated learning facilitates learning across clients without transferr...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.