A review of federated learning algorithms in image classification

Abstract

Image classification is one of the most popular applications of machine learning. It has shown its potential in fields like healthcare, auto-driving and face recognition. Federated learning (FL) emerged in 2017, creating a major innovation to the field. The new structure brings new possibilities, but create new challenges such as data heterogeneity, privacy leakage and communication burdens in parameter updating. The paper solves the problem that there are relatively few papers providing a complete analysis relating to the application of FL in image classification. The paper contributes to describe the new challenges of FL in image classification and show the related reasons behind respectively, then analyze the current state-of-the-art algorithms designed for solving the challenges and improving image model performance by discussing the basic ideas and steps of algorithms and showing their pros and cons. The paper further more contributes to compare the performance of each model in accuracy and communication speed, and outline several possible directions for future advancement.