Add self-paced-training tutorial readme (#3202)

Fixes # .

### Description

A few sentences describing the changes proposed in this pull request.

### Types of changes
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- [x] Non-breaking change (fix or new feature that would not break
existing functionality).
- [ ] Breaking change (fix or new feature that would cause existing
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- [ ] New tests added to cover the changes.
- [ ] Quick tests passed locally by running `./runtest.sh`.
- [ ] In-line docstrings updated.
- [ ] Documentation updated.

Author: chesterxgchen

examples/tutorials/self-paced-training/README.md

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+# Federated Learning with NVIDIA FLARE 
+
+This is a five-part course on Federated Learning with NVIDIA FLARE covers various aspects of federated learning, including how to develop and run federated learning applications, set up and deploy federated learning systems, and understand the privacy and security aspects of federated learning. 
+
+The initial 12-chapter course give you a comprehensive views of the FLARE, from running federated learning application, algorithms, system architecture, experimental treacking, system monitoring to industrial applications. 
+
+ While each notebook can run independently, and you can skip certain chapters or sections, it is recommended to follow them one-by-one in order.
+
+### [Part 1: Introduction to Federated Learning](./part-1_federated_learning_introduction/part_1_introduction.ipynb)
+
+Running and developing federated learning applications using a simulator.
+
+### [Part 2: Federated Learning System](./part-2_federated_learning_system/part-2_introduction.ipynb)
+
+In this part, we dive into NVIDIA FLARE's federated learning/computing system, including system architecture, deployment process, deployment simulation, and interaction with the system.
+
+### [Part 3: Security and Privacy](./part-3_security_and_privacy/part-3_introduction.ipynb)
+
+Once we understand the basics of federated learning applications and federated computing systems, we will dive into other aspects of federated learning applications: privacy and security. We will discuss privacy and security concerns, different Privacy Enhancing Techniques (PETs), as well as enterprise security support.
+
+### [Part 4: Advanced Topics in Federated Learning](./part-4_advanced_federated_learning/part-4_introduction.ipynb)
+
+We will discuss federated learning with advanced topics:
+
+* Different federated learning algorithms such as FedOpt, FedProx, etc.
+* Different federated learning workflows: cyclic, split learning, swarm learning
+* How to train or fine-tune large language models
+* How to train secure federated XGBoost
+* FLARE high-level vs. low-level APIs: dive into low-level but powerful APIs
+
+### [Part 5: Federated Learning in Different Industries](./part-5_federated_learning_applications_in_industries/part-5_introduction.ipynb)
+
+We have covered quite a bit of federated learning techniques. How do we apply them to different training use cases for cancer studies or fraud detection? Part 5 will show you how to use these techniques in different applications.