feat: add dynamic data seed option for flowertune examples

[xiaoyanshen799]

Issue

Description

When max_steps is used in FlowerTune LLM examples, clients repeatedly train on the same data subset every round due to a fixed default seed, causing the remaining data in each client's partition to never be seen during training.

Related issues/PRs

#6808

Proposal

Explanation

• Added train.dynamic-data-seed to pyproject.toml to allow clients to cover more training data across rounds.
  Default is false to maintain backward compatibility.
• Updated client_app.py in each example to dynamically set data_seed when the train.dynamic-data-seed = true:
  base_seed = int(training_arguments.data_seed or training_arguments.seed)
training_arguments.data_seed = base_seed + server_round - 1
• All changes are applied consistently across all four FlowerTune LLM examples (finance, medical, code, general-nlp).

Closes #6808

Checklist

• Implement proposed change
• Write tests
• Update documentation
• Address LLM-reviewer comments, if applicable (e.g., GitHub Copilot)
• Make CI checks pass
• Ping maintainers on Slack (channel #contributions)

Any other comments?

[Copilot]

Pull request overview

Adds an opt-in configuration to vary the per-round data sampling seed in FlowerTune LLM example clients, addressing repeated training on the same early subset when max_steps is used.

Changes:

• Introduces train.dynamic-data-seed = false (default off) in each FlowerTune LLM example pyproject.toml.
• Updates each example client_app.py to (optionally) derive a per-round TrainingArguments.data_seed based on server-round.

Reviewed changes

Copilot reviewed 8 out of 8 changed files in this pull request and generated 4 comments.

Show a summary per file

File	Description
examples/flowertune-llm-medical/pyproject.toml	Adds train.dynamic-data-seed config toggle.
examples/flowertune-llm-medical/flowertune_medical/client_app.py	Optionally sets data_seed per round and reuses server_round variable.
examples/flowertune-llm-general-nlp/pyproject.toml	Adds train.dynamic-data-seed config toggle.
examples/flowertune-llm-general-nlp/flowertune_generalnlp/client_app.py	Optionally sets data_seed per round and reuses server_round variable.
examples/flowertune-llm-finance/pyproject.toml	Adds train.dynamic-data-seed config toggle.
examples/flowertune-llm-finance/flowertune_finance/client_app.py	Optionally sets data_seed per round and reuses server_round variable.
examples/flowertune-llm-code/pyproject.toml	Adds train.dynamic-data-seed config toggle.
examples/flowertune-llm-code/flowertune_code/client_app.py	Optionally sets data_seed per round and reuses server_round variable.

---

💡 Add Copilot custom instructions for smarter, more guided reviews. Learn how to get started.

[Copilot]

base_seed = int(training_arguments.data_seed or training_arguments.seed) treats data_seed=0 as falsy and will incorrectly fall back to seed. Use an explicit is not None check (or getattr default) so 0 remains a valid seed value.

Suggested change

	base_seed = int(training_arguments.data_seed or training_arguments.seed)
	if training_arguments.data_seed is not None:
	base_seed = int(training_arguments.data_seed)
	elif training_arguments.seed is not None:
	base_seed = int(training_arguments.seed)
	else:
	raise ValueError("Neither data_seed nor seed is set in TrainingArguments.")

[Copilot]

base_seed = int(training_arguments.data_seed or training_arguments.seed) treats data_seed=0 as falsy and will incorrectly fall back to seed. Use an explicit is not None check (or getattr default) so 0 remains a valid seed value.

Suggested change

	base_seed = int(training_arguments.data_seed or training_arguments.seed)
	base_seed = int(
	training_arguments.data_seed
	if training_arguments.data_seed is not None
	else training_arguments.seed
	)

[Copilot]

base_seed = int(training_arguments.data_seed or training_arguments.seed) treats data_seed=0 as falsy and will incorrectly fall back to seed. Use an explicit is not None check (or getattr default) so 0 remains a valid seed value.

Suggested change

	base_seed = int(training_arguments.data_seed or training_arguments.seed)
	base_seed_source = (
	training_arguments.data_seed
	if training_arguments.data_seed is not None
	else training_arguments.seed
	)
	base_seed = int(base_seed_source)

[Copilot]

base_seed = int(training_arguments.data_seed or training_arguments.seed) treats data_seed=0 as falsy and will incorrectly fall back to seed. Use an explicit is not None check (or getattr default) so 0 remains a valid seed value.

Suggested change

	base_seed = int(training_arguments.data_seed or training_arguments.seed)
	base_seed = (
	int(training_arguments.data_seed)
	if getattr(training_arguments, "data_seed", None) is not None
	else int(training_arguments.seed)
	)