{
  "paper": {
    "num": 16,
    "title": "Tracing Bias Propagation Across Subgroups in Federated Learning",
    "authors": "Tirathraj Ramburn, Afaf Ta\u00efk, Farnoosh Ghadiri",
    "url": "https://drive.google.com/file/d/1aA6Fac9mYYJa1VoylGh4o3GgjQjDeobk/view?usp=sharing",
    "affiliation": ""
  },
  "inputs": {
    "source error": "30% of A1-B3 positives \u2192 0 at client 0",
    "clients": 4,
    "heterogeneity h": 0.0,
    "\u03b2 (1,Z,R,W1,W2)": "-1,0.85,0.90,-0.70,0.55",
    "A\u2192R shift": 0.25,
    "seeds": 20,
    "analysis": "IID (8 groups, recipients averaged)"
  },
  "tiles": [
    {
      "label": "Rank agreement (Spearman)",
      "value": "0.980",
      "sub": "95% CI [0.974, 0.986] over 20 seeds",
      "tone": "good"
    },
    {
      "label": "Size agreement (slope c)",
      "value": "1.002",
      "sub": "1.0 = measured as large as predicted; CI [0.982, 1.023]",
      "tone": "good"
    },
    {
      "label": "Most affected recipient",
      "value": "A1-B4",
      "sub": "17% more than target A1-B3 (rank 2)",
      "tone": "warn"
    },
    {
      "label": "Labels changed",
      "value": "45.5",
      "sub": "0.379% of 12,000 federation labels; every recipient mean decreased",
      "tone": "accent"
    }
  ],
  "figures": [
    {
      "path": "fig_pred_vs_meas.png",
      "title": "Can we predict who gets hurt without retraining?",
      "caption": "Each point is a recipient subgroup: x = closed-form prediction \u03c1\u00b7\u03bc\u207a\u1d40v_T, y = measured mean logit decrease on unchanged positive test cases after actually retraining (mean and 95% range over seeds)."
    },
    {
      "path": "fig_audit_matrix.png",
      "title": "Audit without retraining: every possible source error",
      "caption": "Rows = which subgroup's positives are mislabelled at client 0; columns = recipient subgroups elsewhere. Boxed = most-hit recipient; dotted = the same-named group. Off-diagonal boxes are the paper's warning."
    },
    {
      "path": "fig_sweep_vector.png",
      "title": "Linearity in \u03c1, and the direction of propagation",
      "caption": "Left: the measured effect grows \u2248 proportionally with the flipped fraction, as Eq. 3 predicts. Right: the propagation vector; recipients whose positive-case means project further along it lose more."
    }
  ],
  "compare": [
    {
      "metric": "Spearman (IID, 30%)",
      "paper": "A1-B3: 0.975 [0.961, 0.987] \u00b7 A0-B2: 0.968 [0.955, 0.980]",
      "yours": "0.980 [0.974, 0.986]",
      "note": "target A1-B3"
    },
    {
      "metric": "Slope c",
      "paper": "A1-B3: 1.014 \u00b7 A0-B2: 1.016 (h=1: 1.009 / 1.007)",
      "yours": "1.002",
      "note": ""
    },
    {
      "metric": "Most affected vs target",
      "paper": "A1-B3 \u2192 A1-B4 hit 18.2% more \u00b7 A0-B2 \u2192 A1-B1 hit 34.9% more (target ranks 4th)",
      "yours": "A1-B4, 17.1% more (target rank 2)",
      "note": ""
    },
    {
      "metric": "Labels changed (30%)",
      "paper": "A1-B3: 45.25 (0.377%) \u00b7 A0-B2: 27.20 (0.227%)",
      "yours": "45.50 (0.379%)",
      "note": ""
    },
    {
      "metric": "Sources whose biggest victim is another group",
      "paper": "\u2014  (new: audit matrix)",
      "yours": "5 of 8",
      "note": "predicted, no retraining"
    }
  ],
  "runbook": {
    "name": "smash-16-fl-bias-propagation",
    "url": ""
  }
}