{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "You already need task-specific training and want to reduce its memory or compute cost.",
    "change": "Change only the training technique on a small controlled run after checking prerequisites.",
    "baseline": "Your current training recipe with the same model, data split, and evaluation.",
    "steps": [
      "Locate the paper's implementation and check its license, model support, data needs, and hardware requirements. Stop here if you cannot obtain the required artifacts.",
      "Save a small set of representative product tasks and expected outcomes. Keep evaluation tasks out of any training or tuning, and choose quality and cost limits before running.",
      "Run the baseline and the proposed change on identical inputs. Keep other settings fixed and repeat paired runs to expose variation.",
      "Save per-task outputs, failures, timings, and all usage costs. Review regressions before expanding the test."
    ],
    "measure": "Held-out quality, peak memory, training time, and full compute cost to the target quality.",
    "decision": "Keep the change only if it meets your preselected quality and latency limits and improves the metric you care about after setup and operating costs. Otherwise retain the baseline. This is an exploration plan, not a validated recommendation."
  },
  "builder_review": null,
  "arxiv_id": "2610.01537",
  "title": "FedFit: Federated Fine-Tuning of LLMs via Vector-Bank Parameterization and Quantization",
  "summary": "FedFit replaces standard federated LoRA aggregation with a disjoint shared vector-bank parameterization, alternating single-bank and joint updates with Residual Spectral Aggregation, plus blockwise quantization with client-side error feedback, to cut FL communication overhead. The cost is substantial implementation complexity: a custom aggregation schedule, error-feedback state per client, and a non-standard adapter parameterization that must be maintained outside mature frameworks. It can fail in non-federated settings (the method provides no benefit without distributed clients), under high client heterogeneity where residual aggregation may degrade, and its convergence and perplexity parity are demonstrated only on Qwen2.5 within the paper's experimental conditions.",
  "rationale": "The technique targets multi-client federated fine-tuning, a deployment scenario the reader with a single 24 GB GPU and no cluster does not operate, so it is worth tracking but not adopting.",
  "technique": "Federated LoRA via vector banks and quantization",
  "family": "treino_eficiente",
  "recommendation": "observar",
  "published": "2026-10-01",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.01537",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "FedFit replaces standard federated LoRA aggregation with a disjoint shared vector-bank parameterization, alternating single-bank and joint updates with Residual Spectral Aggregation, plus blockwise quantization with client-side error feedback, to cut FL communication overhead. The cost is substantial implementation complexity: a custom aggregation schedule, error-feedback state per client, and a non-standard adapter parameterization that must be maintained outside mature frameworks. It can fail in non-federated settings (the method provides no benefit without distributed clients), under high client heterogeneity where residual aggregation may degrade, and its convergence and perplexity parity are demonstrated only on Qwen2.5 within the paper's experimental conditions.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.01537",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "Matches standard federated LoRA perplexity on Qwen2.5 while achieving communication compression ratios up to 100x higher.",
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      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.01537",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
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      "finding": "",
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      "next_check": "",
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    {
      "dimension": "compute",
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      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "latency",
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      "finding": "",
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      "source_page": null,
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    },
    {
      "dimension": "operations",
      "basis": "not_evaluated",
      "finding": "",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "security",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
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      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "data_and_training",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "reproducibility",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "risks": [],
  "minimum_test": [],
  "open_questions": [],
  "independent_tests": [],
  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
  "equations": [],
  "equations_status": "not_fetched",
  "core_kind": "",
  "equations_fetched_at": "",
  "equations_source_url": "",
  "equations_source_sha256": "",
  "exposures_reviewed_at": "",
  "exposures_source_sha256": ""
}