Agentic Federated Learning: The Future of Distributed Training Orchestration explores Autonomous AI agents orchestrated via language models to dynamically manage federated learning, improving privacy and adapting to system dynamics.. Commercial viability score: 4/10 in Agents.
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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/agentic-federated-learning-the-future-of-distributed-training-orchestration
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 agentic-federated-learning-the-future-of-distributed-training-orchestration | Route /paper/agentic-federated-learning-the-future-of-distributed-training-orchestration
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/paper/agentic-federated-learning-the-future-of-distributed-training-orchestrationMCP example
{
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"arguments": {
"arxiv_id": "2604.04895"
}
}source_context
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"paper_ref": "agentic-federated-learning-the-future-of-distributed-training-orchestration",
"topic_slug": null,
"benchmark_ref": null,
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}Paper proof page receipt window
/buildability/agentic-federated-learning-the-future-of-distributed-training-orchestration
Subject: Agentic Federated Learning: The Future of Distributed Training Orchestration
Verdict
Ignore
Verdict is Ignore because current viability and proof state do not clear the buildability gate.
Time to first demo
Insufficient data
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Structured compute envelope
Insufficient data
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Dimensions overall score 4.0
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This equation captures one of the core mathematical components of the system. through Mechanism Design. Each client i has a truly private type, denoted by θi = (bi, ci, qi, bwi)
Page and bbox are available; crop image is pending.
Owned Distribution
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Receipt path
/buildability/agentic-federated-learning-the-future-of-distributed-training-orchestration
Paper ref
agentic-federated-learning-the-future-of-distributed-training-orchestration
arXiv id
2604.04895
Generated at
2026-04-07T20:13:34.907Z
Evidence freshness
fresh
Last verification
2026-04-07T20:13:34.907Z
Sources
0
References
0
Coverage
0%
Lineage hash
9b0b7901d39d03b6a6b78419313be58a61b439c1d19e27dc6c72d18295fd7bd5
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
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Verification pending / evidence receipt incomplete
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paper_evidence_receipts.coverage
This equation captures one of the core mathematical components of the system. Ui(ˆθi|θi) = vi · wi(ˆθi) −C(θi) −Π(audit(ˆθi)) where vi is the value the client a
Page and bbox are available; crop image is pending.
This equation captures one of the core mathematical components of the system. and MNIST datasets under a Dirichlet distribution (α = 0.1) to create a severely Non-IID scenario
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