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Client update를 이용해 프레임워크 내부에서 다양한 client graph를 구성·교체하고, control graph ablation을 통해 Graph-FL gain이 실제 client relation structure 때문인지 아니면 dominance, norm, smoothing, optimizer 효과 때문인지 분해하는 실험 프레임워크.
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Graph-FL Design Lab의 중심은 graph를 만드는 방식과 graph를 검증하는 방식을 같은
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실험대 위에 올리는 것이다. 많은 Graph-FL 방법은 client representation, relation
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score, topology, edge weight, aggregation target 중 일부를 바꾸며 발전한다. 이
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저장소는 그 차이를 공통 부품으로 표현하고, 같은 control과 diagnostic metric으로
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비교할 수 있게 만든다.
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따라서 결과 해석은 단순한 `FedAvg` 대비 성능 비교에서 멈추지 않는다. real graph,
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matched control, graph-free correction, mechanism metric을 함께 읽어 graph gain이
| Single vision run | one assembled graph design or one baseline should be checked quickly |`python run_vision_experiment.py --config <config.json>`|
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| Vision diagnostic suite | real graph, controls, graph-free correction, and diagnostics should be compared together |`python run_vision_suite.py --config <config.json>`|
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| Cora graph ablation | graph-structured input path and summary writer should be checked |`python run_graph_ablation.py --config <config.json>`|
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| Evidence check | a produced result JSON should be checked for schema and attribution fields |`python scripts/checks/result_evidence_bundle.py <result.json> --kind single-run`|
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