{ "schema_version": 1, "builder_plan": { "scenario": "Start with one repeated product task that has a measurable failure or cost.", "change": "Read the methods and identify one change you can isolate in that workflow.", "baseline": "Your current implementation on the same held-out tasks.", "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": "Successful outcomes, response time, human rework, and total cost per successful task.", "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": "2609.16446", "title": "Adaptive Bayesian Partner Selection for Federated Clinical Centers", "summary": "It replaces persistent all-client federated averaging (FedAvg-style global rounds) with per-center peer selection using a Beta-Bernoulli posterior over Shapley utility, UCB ranking, a propose-reject handshake, and optional abstention from communication. The cost is per-center bookkeeping of partner posteriors and pairwise negotiation, plus reliance on the supporting techniques (head personalization, bfloat16 quantized payloads) for the reported efficiency; accuracy is only matched, not improved. It can fail when centers are few or large, when Shapley utility estimates are noisy or expensive to obtain, or when peer non-IID drift invalidates the posterior faster than it concentrates, and the evidence base is a single binary ICU mortality task on one dataset.", "rationale": "The method targets multi-institution federated deployments with many small clients, a setting the reader does not operate, so it is worth tracking rather than adopting or testing on a single-GPU budget.", "technique": "Bayesian peer selection for federated learning", "family": "outro", "recommendation": "observar", "published": "2026-09-15", "as_of": "2026-10-18", "editorial_status": "indexed", "source_url": "https://arxiv.org/abs/2609.16446", "independent_implementations": 0, "total_implementations": 0, "stars": 0, "citations": 0, "claims": [ { "claim_id": "claim-01", "statement": "It replaces persistent all-client federated averaging (FedAvg-style global rounds) with per-center peer selection using a Beta-Bernoulli posterior over Shapley utility, UCB ranking, a propose-reject handshake, and optional abstention from communication. The cost is per-center bookkeeping of partner posteriors and pairwise negotiation, plus reliance on the supporting techniques (head personalization, bfloat16 quantized payloads) for the reported efficiency; accuracy is only matched, not improved. It can fail when centers are few or large, when Shapley utility estimates are noisy or expensive to obtain, or when peer non-IID drift invalidates the posterior faster than it concentrates, and the evidence base is a single binary ICU mortality task on one dataset.", "basis": "inferred", "result": "", "baseline": "", "conditions": "", "source_url": "https://arxiv.org/abs/2609.16446", "source_page": null, "source_excerpt": "" }, { "claim_id": "claim-02", "statement": "ABPS-X matches the strongest federated baseline (FedDyn, AUROC 0.758) at 0.09x the communication cost of FedAvg on 230 non-IID clinical centers derived from MIMIC-IV.", "basis": "inferred", "result": "", "baseline": "", "conditions": "", "source_url": "https://arxiv.org/abs/2609.16446", "source_page": null, "source_excerpt": "" } ], "exposure_map": [ { "dimension": "quality", "basis": "not_evaluated", "finding": "", "interpretation": "", "limitation": "", "next_check": "", "source_url": "", "source_page": null, "source_excerpt": "" }, { "dimension": "compute", "basis": "not_evaluated", "finding": "", "interpretation": "", "limitation": "", "next_check": "", "source_url": "", "source_page": null, "source_excerpt": "" }, { "dimension": "latency", "basis": "not_evaluated", "finding": "", "interpretation": "", "limitation": "", "next_check": "", "source_url": "", "source_page": null, "source_excerpt": "" }, { "dimension": "operations", "basis": "not_evaluated", "finding": "", "interpretation": "", "limitation": "", "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": "", "limitation": "", "next_check": "", "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_formula", "core_kind": "algorithm", "equations_fetched_at": "2026-10-14", "equations_source_url": "", "equations_source_sha256": "", "exposures_reviewed_at": "", "exposures_source_sha256": "" }