Federated Traditional Machine Learning Algorithms Implementation Guide

This white paper discusses federated traditional machine learning algorithms, focusing on their formulation and implementation. It outlines the advantages of federated learning, particularly its ability to decouple model training from direct access to raw training data, thereby addressing communication costs associated with decentralized data. The document details various traditional machine learning methods applicable to federated learning, including linear models, k-means clustering, non-linear SVM, random forests, and XGBoost. Each method is explored in terms of what information clients share with the server, how the server aggregates this information, and the subsequent actions clients must take with the global model. The paper emphasizes the need for careful consideration at both the algorithm and implementation levels, highlighting the importance of library support and the potential for model ensembles. The conclusion reiterates the flexibility in formulating federated traditional machine learning algorithms compared to deep learning, while also noting the variability in optimization methods across different algorithms.