SubFLOT: Submodel Extraction for Efficient and Personalized Federated Learning via Optimal Transport
Compared to this week’s papers
Verification pending
Use This Via API or MCP
Use Signal Canvas as the narrative proof surface
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Page Freshness
Signal Canvas proof surface
Canonical route: /signal-canvas/subflot-submodel-extraction-for-efficient-and-personalized-federated-learning-via-optimal-transport
- Proof freshness
- stale
- Proof status
- unverified
- Display score
- 7/10
- Last proof check
- 2026-04-10
- Score updated
- 2026-04-09
- Score fresh until
- 2026-05-09
- References
- 61
- Source count
- 3
- Coverage
- 67%
This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.
Agent Handoff
SubFLOT: Submodel Extraction for Efficient and Personalized Federated Learning via Optimal Transport
Canonical ID subflot-submodel-extraction-for-efficient-and-personalized-federated-learning-via-optimal-transport | Route /signal-canvas/subflot-submodel-extraction-for-efficient-and-personalized-federated-learning-via-optimal-transport
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/subflot-submodel-extraction-for-efficient-and-personalized-federated-learning-via-optimal-transportMCP example
{
"tool": "search_signal_canvas",
"arguments": {
"mode": "paper",
"paper_ref": "subflot-submodel-extraction-for-efficient-and-personalized-federated-learning-via-optimal-transport",
"query_text": "Summarize SubFLOT: Submodel Extraction for Efficient and Personalized Federated Learning via Optimal Transport"
}
}source_context
{
"surface": "signal_canvas",
"mode": "paper",
"query": "SubFLOT: Submodel Extraction for Efficient and Personalized Federated Learning via Optimal Transport",
"normalized_query": "2604.06631",
"route": "/signal-canvas/subflot-submodel-extraction-for-efficient-and-personalized-federated-learning-via-optimal-transport",
"paper_ref": "subflot-submodel-extraction-for-efficient-and-personalized-federated-learning-via-optimal-transport",
"topic_slug": null,
"benchmark_ref": null,
"dataset_ref": null
}Evidence Receipt
Route status: buildingClaims: 0
References: 61
Proof: Verification pending
Freshness state: computing
Source paper: SubFLOT: Submodel Extraction for Efficient and Personalized Federated Learning via Optimal Transport
PDF: https://arxiv.org/pdf/2604.06631v1
Repository: https://github.com/cvpr-org/author-kit
Source count: 3
Coverage: 67%
Last proof check: 2026-04-10T00:15:12.991Z
Signal Canvas receipt window
Ready for execution: SubFLOT: Submodel Extraction for Efficient and Personalized Federated Learning via Optimal Transport
/buildability/subflot-submodel-extraction-for-efficient-and-personalized-federated-learning-via-optimal-transport
Subject: SubFLOT: Submodel Extraction for Efficient and Personalized Federated Learning via Optimal Transport
Verdict
Build Now
Verdict is Build Now because viability and implementation proof cleared the Wave 1 scaffold thresholds.
Time to first demo
Insufficient data
No first-demo timestamp, owner estimate, or elapsed demo receipt is attached to this surface.
Compute envelope
Structured compute envelope
Insufficient data
No data, compute, hardware, memory, latency, dependency, or serving requirement receipt is attached.
Evidence ids
Receipt path
/buildability/subflot-submodel-extraction-for-efficient-and-personalized-federated-learning-via-optimal-transport
Paper ref
subflot-submodel-extraction-for-efficient-and-personalized-federated-learning-via-optimal-transport
arXiv id
2604.06631
Freshness
Generated at
2026-04-10T00:15:12.991Z
Evidence freshness
stale
Last verification
2026-04-10T00:15:12.991Z
Sources
3
References
61
Coverage
67%
Hash state
Lineage hash
fb391407f8a2a3b47a5f0147cdaf27b4fe6dd44ed473d8348562ef27ed7f5577
Canonical opportunity-kernel lineage hash.
Signature state
External signature
unsigned_external
No founder, registry, pilot, or production-adoption signature is attached to this receipt.
Verification
not_verified
Verification is blocked until an external signature is provided.
Blockers
- Missing: proof_status
- Missing: paper_extraction_scorecards
- Unknown: proof verification has not been recorded yet
61 refs / 3 sources / Verification pending
proof_status
paper_extraction_scorecards
Paper Conversation
Citation-first answers with explicit evidence receipts, disagreement handling, commercialization framing, and next actions.
SubFLOT: Submodel Extraction for Efficient and Personalized Federated Learning via Optimal Transport
Canonical Paper Receipt
Last verification: 2026-04-10T00:15:12.991ZFreshness: stale
Proof: unverified
Repo: active
References: 61
Sources: 3
Coverage: 67%
- - proof_status
- - paper_extraction_scorecards
- - proof verification has not been recorded yet
Preparing verified analysis
Dimensions overall score 7.0
GitHub Code Pulse
Claim map
No public claim map is available for this paper yet.
Startup potential card
Related Resources
- Federated Learning(glossary)
- Hierarchical Federated Learning(glossary)
- What are the considerations for optimizing NLP models in a federated learning setting?(question)
- How can machine unlearning be applied to federated learning systems?(question)
- How can graph neural networks be adapted for federated learning scenarios on distributed graph data?(question)
BUILDER'S SANDBOX
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