NVFlare / auto-fl-research
AGENTS.md1k starsChanged 12 days agoApache-2.0View on GitHub
Start with program.md. It is the general research-org entry point for the agent. Then read the active task profile. If the human does not specify one, use tasks/cifar10/profile.md. Use the active task's mutation_schema.yaml only when program.md or the active task profile directs you to the hard mutation bounds, or when choosing a mutation axis. If anything here conflicts with program.md, follow program.md unless a human explicitly overrides it. If a task profile conflicts with generic program.md text on task budget,…
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Who's talking about NVIDIA/NVFlare
- Kaggle/kaggle-sdk-python“kagglesdk 0.1.31 and 0.1.32 import a missing competitions.legacy package”GitHub · Jun 30, 2026 · 1 reactions
- londonaicentre/FLIP“docs: call the NVIDIA framework FLARE, its short official name”GitHub · Oct 17, 2026
- NVIDIA/SkillEvaluator“[BUG]: Generated BENCHMARK.md does not faithfully explain a passing gate and splits canonical case IDs”GitHub · Oct 9, 2026
- Denimworld12/Hospital-Chain“feat: run hospital nodes on separate machines with provisioning kits and a cross-address verifier”GitHub · Oct 2, 2026
- ai4os/ai4os-nvflare“Update NVFlare version to 2.8”GitHub · Jul 16, 2026
The biggest 5 of 25 mentions linking to the repository that our crawl found. Each line is their own headline.
See all 25
Talked about on GitHub (25 times).
On GitHub · 25
- Jun 30, 2026kagglesdk 0.1.31 and 0.1.32 import a missing competitions.legacy package — Kaggle/kaggle-sdk-python1 reaction · 2 comments
- Oct 17, 2026docs: call the NVIDIA framework FLARE, its short official name — londonaicentre/FLIP0 reactions · 1 comment
- Oct 9, 2026[BUG]: Generated BENCHMARK.md does not faithfully explain a passing gate and splits canonical case IDs — NVIDIA/SkillEvaluatorsays it tested it0 reactions · 1 comment
- Oct 2, 2026feat: run hospital nodes on separate machines with provisioning kits and a cross-address verifier — Denimworld12/Hospital-Chain0 reactions
- Jul 16, 2026Update NVFlare version to 2.8 — ai4os/ai4os-nvflare0 reactions
20 more on GitHub
- Jul 6, 2026Upgrade NVFlare backbone 2.7.2 → 2.8.0 (deferred until a clean 2.7.2 run) — KatherLab/MediSwarm0 reactions · 3 comments
- Jun 26, 2026Upgrade NVFLARE backend 2.7.2 → 2.8.0 — londonaicentre/FLIP0 reactions
- Jun 22, 2026Add step-ca ephemeral admin certs — pcnudde/NVFlare0 reactions · 1 comment
- May 7, 2026feat(privacy): federated learning architecture-ready (Horizon Europe / EU4Health alignment) — Yanstart/VarunaPoC0 reactions · 1 comment
- Jan 7, 2026T3: Model fitting (deploying GWAS tool / model on every client) [Enamul, ++] — collaborativebioinformatics/FedGen0 reactions
- Sep 10, 2025[Feature]: From Vertical FL feasibility (#363) to first example — londonaicentre/FLIP0 reactions · 1 comment
- Jun 23, 2025Secure xgboost example — RhinoHealth/user-resources0 reactions · 3 comments
- May 14, 2025NVFlare backbone upgrade for better connection performance — KatherLab/MediSwarm0 reactions · 2 comments
- Mar 24, 2025NVFlare Integration — KCL-BMEIS/UltraFlwr0 reactions
- Jul 14, 2024Try to fix experiment already exist issue in `MLFlowHandler` — Project-MONAI/MONAI0 reactions · 11 comments
- Sep 13, 2023Add stats_sender to MonaiAlgo for FL stats — Project-MONAI/MONAI0 reactions · 1 comment
- Aug 9, 2023【CS-part3】New submissions for Wed, 9 Aug 23 — Yukeaaa/arxiv-daily0 reactions
- Jul 12, 2023New learner cifar10 — yanchengnv/NVFlare0 reactions
- May 6, 2023Bump nvflare from 2.0.10 to 2.3.0 in /federated_learning — NVIDIA/fsi-samples0 reactions · 1 comment
- Mar 22, 2023[WIP] add `FLStatsHandler` — Project-MONAI/MONAI0 reactions · 3 comments
- Mar 21, 2023Try NVFlare cifar10 example — KatherLab/swarm-learning-hpe0 reactions
- Mar 21, 2023Apply the new data split strategies — KatherLab/swarm-learning-hpe0 reactions
- Dec 6, 2022How to use bst.eval_set() and bst.update() with xgboost_ray — ray-project/xgboost_ray0 reactions · 3 comments
- May 18, 2022713 Update nvflare example for 2.1 — Project-MONAI/tutorials0 reactions · 2 comments
- Mar 22, 2022Bump nvflare from 2.0.2 to 2.0.16 in /federated_learning/nvflare/nvflare_spleen_example — Project-MONAI/tutorials0 reactions · 4 comments
Where files are headingNew AGENTS.md files have more rules about keeping secrets out of the code, and fewer about code style.
Of the AGENTS.md files we read, the 31,439 first seen in the last six months, against the older ones: the kind of rule that gained most share, and the kind that lost most. About files like this in general, not this one, and an estimate, since a kind is a pattern in the words.
In this repository16 other files it gives its agents.
- AGENTS.mdAGENTS.md
- examples/tutorials/self-paced-training/AGENTS.mdAGENTS.md
- CLAUDE.mdCLAUDE.md
- research/auto-fl-research/CLAUDE.mdCLAUDE.md
- autofl-nvflare-reportSkill
- autofl-nvflareSkill
- nvflare-autofl-reportSkill
- nvflare-autoflSkill
- nvflare-convert-huggingfaceSkill
- nvflare-convert-lightningSkill
- nvflare-convert-pytorchSkill
- nvflare-diagnose-jobSkill
- nvflare-fed-statsSkill
- nvflare-orientSkill
- nvflare-sharedSkill
- nvflare-example-skillSkill
The file
We split this AGENTS.md into the rules it gives an agent
4 rules. About 0 are common, 1 is distinctive, and 3 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈1
Distinctive · estimate
This author's own choices. Worth a look.
3
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 5
Start with program.md. It is the general research-org entry point for the agent.
- Distinctive: line 23
Do not change unless explicitly requested:
- Very specific: line 43
Run the active task profile's validation command, with TASK_DIR set to the active task.
- Very specific: line 44
Run the active task profile's smoke command.
≈ “Common” is an estimate: at least 1 in 5 of the 40,246 AGENTS.md files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
AGENTS.md
Entry point
Start with program.md. It is the general research-org entry point for the agent.
Then read the active task profile. If the human does not specify one, use
tasks/cifar10/profile.md.
Use the active task's mutation_schema.yaml only when program.md or the active task profile directs you to the hard mutation bounds, or when choosing a mutation axis. If anything here conflicts with program.md, follow program.md unless a human explicitly overrides it. If a task profile conflicts with generic program.md text on task budget, environment, metric, or edit surface, follow the task profile.
Mission
Improve this Auto-FL NVFlare harness without breaking the federated contract.
Files you may edit
Preferred mutation files:
- task-local
client.py - shared
tasks/shared/custom_aggregators.py - task-local
job.py - task-local
model.pyfor registered architecture variants under the active parameter cap
Do not change unless explicitly requested:
- shared
data/*or task-local data bridge files
Hard invariants
You must preserve all of the following unless a human explicitly asks for a protocol upgrade:
flare.init()while flare.is_running():input_model = flare.receive()flare.send(output_model)model.load_state_dict(input_model.params, strict=True)compute_model_diff(...)output_model.params_type == ParamsType.DIFFoutput_model.meta["NUM_STEPS_CURRENT_ROUND"]- the optional
if flare.is_evaluate():branch - the same selected
model_archmust be instantiated on server and clients for a run model_archandmax_model_paramsare fixed budget fields unless the campaign is explicitly labeled as an architecture subcampaign
Required workflow after every edit
- Run the active task profile's validation command, with
TASK_DIRset to the active task. - Run the active task profile's smoke command. For non-CIFAR tasks, pass the task-specific
SMOKE_ARGSor runbash scripts/run_iteration.sh ...with the active task budget. - Record the mutation in
results.tsv - Summarize the mutation in
templates/mutation_report.md
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

