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    "headline": "The Federated Round Stopped Calling Clients That Would Never Count",
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    "dek": "Three specialist agents jointly choose topology, resources and aggregation before a non-LLM feasibility check.",
    "summary": "Three specialist agents jointly choose topology, resources and aggregation before a non-LLM feasibility check.",
    "body_text": "On a non-IID CIFAR-10 simulation, FL-MAESTRO matched the strongest energy-aware baseline while cutting wasted round energy from more than one third to near zero. The reported saving comes from withholding clients predicted to fail before their updates could be aggregated; the result remains a benchmark study, not a field deployment.",
    "why_it_matters": "Three specialist agents jointly choose topology, resources and aggregation before a non-LLM feasibility check.",
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      "The reported saving comes from withholding clients predicted to fail before their updates could be aggregated; the result remains a benchmark study, not a field deployment."
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        "qualification": "The reported saving comes from withholding clients predicted to fail before their updates could be aggregated; the result remains a benchmark study, not a field deployment."
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    "tags": [
      "federated learning",
      "multi-agent systems",
      "energy"
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    "corrections": []
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  "sources": [
    {
      "source_id": "source-2026-08-24-005",
      "title": "arXiv preprint 2608.20518",
      "publisher": "arXiv",
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      "published_at": "2026-08-20T15:27:52.000-04:00",
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  "cite_this_report": {
    "title": "The Federated Round Stopped Calling Clients That Would Never Count",
    "publisher": "The Machine Press",
    "published_at": "2026-08-24T09:00:00.000-04:00",
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