  Over the past few years, significant advancements have been made in the field
of machine learning (ML) to address resource management, interference
management, autonomy, and decision-making in wireless networks. Traditional ML
approaches rely on centralized methods, where data is collected at a central
server for training. However, this approach poses a challenge in terms of
preserving the data privacy of devices. To address this issue, federated
learning (FL) has emerged as an effective solution that allows edge devices to
collaboratively train ML models without compromising data privacy. In FL, local
datasets are not shared, and the focus is on learning a global model for a
specific task involving all devices. However, FL has limitations when it comes
to adapting the model to devices with different data distributions. In such
cases, meta learning is considered, as it enables the adaptation of learning
models to different data distributions using only a few data samples. In this
tutorial, we present a comprehensive review of FL, meta learning, and federated
meta learning (FedMeta). Unlike other tutorial papers, our objective is to
explore how FL, meta learning, and FedMeta methodologies can be designed,
optimized, and evolved, and their applications over wireless networks. We also
analyze the relationships among these learning algorithms and examine their
advantages and disadvantages in real-world applications.
