FED-FSTQ: Fisher-Guided Token Quantization for Communication-Efficient Federated Fine-Tuning of LLMs on Edge Devices
Compared to this week’s papers
Verification pending
Use This Via API or MCP
Use Signal Canvas as the narrative proof surface
Signal Canvas is the citation-first public layer for turning one paper into a structured commercialization narrative. Use it to hand off into REST, MCP, Build Loop, and launch-pack execution without losing source lineage.
Page Freshness
Signal Canvas proof surface
Canonical route: /signal-canvas/fed-fstq-fisher-guided-token-quantization-for-communication-efficient-federated-fine-tuning-of-llms-on-edge-devices
- Proof freshness
- fresh
- Proof status
- unverified
- Display score
- 8/10
- Last proof check
- 2026-04-29
- Score updated
- 2026-04-29
- Score fresh until
- 2026-05-29
- References
- 0
- Source count
- 3
- Coverage
- 50%
Page-specific freshness sourced from this paper's evidence receipt and score bundle.
Agent Handoff
FED-FSTQ: Fisher-Guided Token Quantization for Communication-Efficient Federated Fine-Tuning of LLMs on Edge Devices
Canonical ID fed-fstq-fisher-guided-token-quantization-for-communication-efficient-federated-fine-tuning-of-llms-on-edge-devices | Route /signal-canvas/fed-fstq-fisher-guided-token-quantization-for-communication-efficient-federated-fine-tuning-of-llms-on-edge-devices
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/fed-fstq-fisher-guided-token-quantization-for-communication-efficient-federated-fine-tuning-of-llms-on-edge-devicesMCP example
{
"tool": "search_signal_canvas",
"arguments": {
"mode": "paper",
"paper_ref": "fed-fstq-fisher-guided-token-quantization-for-communication-efficient-federated-fine-tuning-of-llms-on-edge-devices",
"query_text": "Summarize FED-FSTQ: Fisher-Guided Token Quantization for Communication-Efficient Federated Fine-Tuning of LLMs on Edge Devices"
}
}source_context
{
"surface": "signal_canvas",
"mode": "paper",
"query": "FED-FSTQ: Fisher-Guided Token Quantization for Communication-Efficient Federated Fine-Tuning of LLMs on Edge Devices",
"normalized_query": "2604.25421",
"route": "/signal-canvas/fed-fstq-fisher-guided-token-quantization-for-communication-efficient-federated-fine-tuning-of-llms-on-edge-devices",
"paper_ref": "fed-fstq-fisher-guided-token-quantization-for-communication-efficient-federated-fine-tuning-of-llms-on-edge-devices",
"topic_slug": null,
"benchmark_ref": null,
"dataset_ref": null
}Evidence Receipt
Route status: buildingClaims: 0
References: Pending verification
Proof: Verification pending
Freshness state: computing
Source paper: FED-FSTQ: Fisher-Guided Token Quantization for Communication-Efficient Federated Fine-Tuning of LLMs on Edge Devices
PDF: https://arxiv.org/pdf/2604.25421v1
Source count: 3
Coverage: 50%
Last proof check: 2026-04-29T02:43:59.454Z
Signal Canvas receipt window
Watch and verify: FED-FSTQ: Fisher-Guided Token Quantization for Communication-Efficient Federated Fine-Tuning of LLMs on Edge Devices
/buildability/fed-fstq-fisher-guided-token-quantization-for-communication-efficient-federated-fine-tuning-of-llms-on-edge-devices
Subject: FED-FSTQ: Fisher-Guided Token Quantization for Communication-Efficient Federated Fine-Tuning of LLMs on Edge Devices
Verdict
Watch
Verdict is Watch because viability or proof quality is intermediate and should be re-evaluated before execution.
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/fed-fstq-fisher-guided-token-quantization-for-communication-efficient-federated-fine-tuning-of-llms-on-edge-devices
Paper ref
fed-fstq-fisher-guided-token-quantization-for-communication-efficient-federated-fine-tuning-of-llms-on-edge-devices
arXiv id
2604.25421
Freshness
Generated at
2026-04-29T02:43:59.454Z
Evidence freshness
fresh
Last verification
2026-04-29T02:43:59.454Z
Sources
3
References
0
Coverage
50%
Hash state
Lineage hash
c3e64a7ae37104dd23398fb9ee0a5151d371bc0e6869b6552b96aec00c82f3ca
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: repo_url
- Missing: references
- Missing: proof_status
- Unknown: proof verification has not been recorded yet
Pending verification refs / 3 sources / Verification pending
repo_url
references
Paper Conversation
Citation-first answers with explicit evidence receipts, disagreement handling, commercialization framing, and next actions.
FED-FSTQ: Fisher-Guided Token Quantization for Communication-Efficient Federated Fine-Tuning of LLMs on Edge Devices
Canonical Paper Receipt
Last verification: 2026-04-29T02:43:59.454ZFreshness: fresh
Proof: unverified
Repo: missing
References: 0
Sources: 3
Coverage: 50%
- - repo_url
- - references
- - proof_status
- - proof verification has not been recorded yet
Preparing verified analysis
Dimensions overall score 8.0
GitHub Code Pulse
No public code linked for this paper yet.
Claim map
No public claim map is available for this paper yet.
Startup potential card
Related Resources
Related resources will appear here when this paper maps cleanly to topic, benchmark, or dataset surfaces.
BUILDER'S SANDBOX
Build This Paper
Use an AI coding agent to implement this research.
Lightweight coding agent in your terminal.
Agentic coding tool for terminal workflows.
AI agent mindset installer and workflow scaffolder.
AI-first code editor built on VS Code.
Free, open-source editor by Microsoft.
Recommended Stack
Startup Essentials
MVP Investment
6mo ROI
0.5-1x
3yr ROI
6-15x
GPU-heavy products have higher costs but premium pricing. Expect break-even by 12mo, then 40%+ margins at scale.