In the distributed collaborative machine learning (DCML) paradigm, federated
learning (FL) recently attracted much attention due to its applications in
health, finance, and the latest innovations such as industry 4.0 and smart
vehicles. FL provides privacy-by-design. It trains a machine learning model
collaboratively over several distributed clients (ranging from two to millions)
such as mobile phones, without sharing their raw data with any other
participant. In practical scenarios, all clients do not have sufficient
computing resources (e.g., Internet of Things), the machine learning model has
millions of parameters, and its privacy between the server and the clients
while training/testing is a prime concern (e.g., rival parties). In this
regard, FL is not sufficient, so split learning (SL) is introduced. SL is
reliable in these scenarios as it splits a model into multiple portions,
distributes them among clients and server, and trains/tests their respective
model portions to accomplish the full model training/testing. In SL, the
participants do not share both data and their model portions to any other
parties, and usually, a smaller network portion is assigned to the clients
where data resides. Recently, a hybrid of FL and SL, called splitfed learning,
is introduced to elevate the benefits of both FL (faster training/testing time)
and SL (model split and training). Following the developments from FL to SL,
and considering the importance of SL, this chapter is designed to provide
extensive coverage in SL and its variants. The coverage includes fundamentals,
existing findings, integration with privacy measures such as differential
privacy, open problems, and code implementation.

该文章探讨了分散协作机器学习（DCML）范式下分布式学习（FL）和分裂学习（SL）的应用，以及它们与隐私的关系和代码实现。