proof blockPage 268%
This equation captures one of the core mathematical components of the system. client i(i ∈[N]) holds a local dataset Di. For the personal-
Page and bbox are available; crop image is pending.
SubFLOT: Submodel Extraction for Efficient and Personalized Federated Learning via Optimal Transport explores A framework for personalized federated pruning that creates customized submodels using optimal transport and adaptive regularization to improve efficiency on resource-constrained devices.. Commercial viability score: 7/10 in Federated Learning.
Use This Via API or MCP
This route is the stable paper-level surface for citations, viability, references, and downstream handoffs. Use it as the proof layer behind Signal Canvas, workspace creation, and launch-pack generation.
Page Freshness
Canonical route: /paper/subflot-submodel-extraction-for-efficient-and-personalized-federated-learning-via-optimal-transport
This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.
Agent Handoff
Canonical ID subflot-submodel-extraction-for-efficient-and-personalized-federated-learning-via-optimal-transport | Route /paper/subflot-submodel-extraction-for-efficient-and-personalized-federated-learning-via-optimal-transport
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/paper/subflot-submodel-extraction-for-efficient-and-personalized-federated-learning-via-optimal-transportMCP example
{
"tool": "get_paper",
"arguments": {
"arxiv_id": "2604.06631"
}
}source_context
{
"surface": "paper",
"mode": "paper",
"query": "SubFLOT: Submodel Extraction for Efficient and Personalized Federated Learning via Optimal Transport",
"normalized_query": "2604.06631",
"route": "/paper/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
}Paper proof page receipt window
/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.
Structured compute envelope
Insufficient data
No data, compute, hardware, memory, latency, dependency, or serving requirement receipt is attached.
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
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%
Lineage hash
fb391407f8a2a3b47a5f0147cdaf27b4fe6dd44ed473d8348562ef27ed7f5577
Canonical opportunity-kernel lineage hash.
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.
61 refs / 3 sources / Verification pending
proof_status
paper_extraction_scorecards
Constellation, claims, and market context stay visible on the paper proof page even when commercialization rails are held back for incomplete proof receipts.
Research neighborhood
Interactive graph renders after load.
Preparing verified analysis
Dimensions overall score 7.0
No public claim map is available for this paper yet.
Visual citation anchors from the paper document graph.
This equation captures one of the core mathematical components of the system. client i(i ∈[N]) holds a local dataset Di. For the personal-
Page and bbox are available; crop image is pending.
This equation captures one of the core mathematical components of the system. ity). We compute a cost matrix C(l) ∈Rdl×d′
Page and bbox are available; crop image is pending.
This equation captures one of the core mathematical components of the system. we have ∥∇Fi(W1) −∇Fi(W2)∥≤L ∥W1 −W2∥.
Page and bbox are available; crop image is pending.
No public competitor map is available for this paper yet.
Owned Distribution
Get the weekly shortlist of commercializable papers, benchmark movers, and proof receipts that matter for product execution.
References are not available from the internal index yet.