fix: example restoration of CIFAR100 fedavg

[Spiral-Memory]

What type of PR is this?

/kind bug
/kind cleanup
/kind documentation

What this PR does / why we need it:

This PR provides a comprehensive fix for the federated_learning/fedavg example, which was previously unusable for new community members. The example was plagued by a cascade of issues including hardcoded absolute paths, undocumented environmental requirements (Python 3.10), a runtime code bug, and a complete lack of documentation.

This contribution restores the example to a fully functional and portable state. It introduces a centralized configuration system (config.py) to dynamically handle file paths, adds a README.md with clear setup instructions, includes a requirements.txt for a reproducible environment, and fixes the underlying code bug in basemodel.py.

Which issue(s) this PR fixes:

Fixes #252

2025-08-24.00-24-08.mp4

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Welcome @Spiral-Memory! It looks like this is your first PR to kubeedge/ianvs 🎉

[kubeedge-bot]

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[gemini-code-assist]

Summary of Changes

Hello @Spiral-Memory, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request comprehensively addresses several critical issues within the federated_learning/fedavg example for CIFAR-100, making it fully functional and portable. It resolves problems stemming from hardcoded absolute paths, missing environmental requirements, a runtime bug, and a lack of clear documentation, ensuring new community members can easily use and reproduce the example.

Highlights

• Centralized Path Management: Introduced config.py to define and manage all file paths dynamically, eliminating hardcoded absolute paths across various configuration files.
• Enhanced Documentation: Added a README.md with clear, step-by-step instructions for setting up the environment, configuring paths, preparing the dataset, and running the example.
• Reproducible Environment: Included a requirements.txt file to specify necessary Python dependencies, ensuring a consistent and reproducible environment.
• Runtime Bug Fix: Corrected a critical bug in basemodel.py within the predict method, which previously prevented the example from running correctly.
• Path Standardization: Updated numerous YAML configuration files (algorithm.yaml, benchmarkingjob.yaml, testenv.yaml) to utilize placeholders managed by config.py for model, output, and dataset index paths.
• Utility Script Integration: Modified utils.py to leverage the new centralized path configurations for dataset processing and saving.

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[gemini-code-assist]

Code Review

This pull request is a great improvement for the federated_learning/fedavg example, making it portable and usable by removing hardcoded paths and adding necessary documentation and configuration. The introduction of config.py to manage paths and the fix in basemodel.py are excellent changes. I've found a critical issue in the new requirements.txt with an invalid package version, and a medium-severity issue in the new README.md where the instructions could be clearer. Addressing these will make the example fully functional as intended.

[Spiral-Memory]

/assign

[AryanNanda17]

@Spiral-Memory, have you tested out the changes?

[AryanNanda17]

Can you please share screenshot of output?

[Spiral-Memory]

Hi @AryanNanda17, Yes, I've tested the changes when I made this change. I've attached a video in the PR description; please have a look at that. Thanks.

[AryanNanda17]

Can you please run it for 2 epochs and then share?

[AryanNanda17]

Please give sample output here.