fedavg-federated-noniid-mnist

analysis.R
dataset_manifest.json
example_client_bias.csv
outputs

{
  "manifest_version": "1.0",
  "dataset": {
    "name": "MNIST",
    "source": "torchvision.datasets.MNIST",
    "download_url": "http://yann.lecun.com/exdb/mnist/",
    "train_size": 60000,
    "test_size": 10000,
    "n_classes": 10,
    "per_class_train": 6000
  },
  "simulation": {
    "n_clients": 100,
    "n_byzantine": 10,
    "clients_per_round": 20,
    "n_rounds": 8,
    "param_dim": 256,
    "trimmed_mean": {
      "space": "coordinate",
      "trim_k_default": 2
    },
    "fedavg": {
      "weighting": "by_num_samples"
    },
    "update_generation": {
      "w_star": "rnorm(param_dim)",
      "class_prototypes": "matrix(rnorm(n_classes * param_dim), nrow=n_classes, ncol=param_dim)",
      "honest_update": "signal_scale*w_star + bias_strength*(bias_row %*% class_prototypes) + rnorm(param_dim, 0, honest_noise_sd)",
      "honest_noise_sd": 0.08
    }
  },
  "partition": {
    "method": "dirichlet_per_class",
    "alpha_default": 0.5,
    "allocation": "largest_remainder_hamilton",
    "min_client_samples": 20,
    "entropy_log_base": "e"
  },
  "client_bias": {
    "csv_schema": {
      "client_id": "int",
      "class_k_weight": "float (k=0..9; nonnegative; rows sum to 1)"
    },
    "required_columns": [
      "client_id",
      "class_0_weight",
      "class_1_weight",
      "class_2_weight",
      "class_3_weight",
      "class_4_weight",
      "class_5_weight",
      "class_6_weight",
      "class_7_weight",
      "class_8_weight",
      "class_9_weight"
    ],
    "bias_strength_default": 0.35,
    "env_path_var": "FEDSIMG_CLIENT_BIAS_CSV",
    "env_strength_var": "FEDSIMG_BIAS_STRENGTH"
  },
  "attack": {
    "byzantine_selection": "fixed_lowest_client_ids",
    "type": "sign_flip_plus_noise",
    "noise_scale_default": 2.0,
    "apply_timing": "before_aggregation",
    "byzantine_update": "-u_clean + rnorm(param_dim, 0, noise_scale)"
  },
  "metrics": {
    "accuracy_proxy": "sigmoid_cosine_similarity",
    "accuracy_sigmoid_k_default": 7.5,
    "accuracy_sigmoid_b_default": 2.5,
    "round_noise_sd": 0.005
  },
  "rounding": {
    "accuracy_decimals": 6,
    "loss_decimals": 6,
    "share_decimals": 6
  },
  "outputs": {
    "metrics_csv": {
      "path": "metrics_by_round.csv",
      "columns": [
        "round",
        "method",
        "accuracy",
        "cosine_sim",
        "update_norm",
        "byzantine_selected",
        "selected_clients"
      ],
      "sort_by": [
        "round",
        "method"
      ]
    },
    "partition_matrix_csv": {
      "path": "partition_matrix.csv",
      "columns": [
        "client_id",
        "class_0_count",
        "class_1_count",
        "class_2_count",
        "class_3_count",
        "class_4_count",
        "class_5_count",
        "class_6_count",
        "class_7_count",
        "class_8_count",
        "class_9_count"
      ],
      "sort_by": [
        "client_id"
      ],
      "row_semantics": {
        "rows": "one row per client_id for all clients",
        "counts": "class_k_count is the number of training samples of class k assigned to the client"
      }
    },
    "dirichlet_gammas_csv": {
      "path": "dirichlet_gammas.csv",
      "columns": [
        "class",
        "client_id",
        "gamma_raw",
        "p",
        "allocated_count"
      ],
      "sort_by": [
        "class",
        "client_id"
      ],
      "row_semantics": {
        "rows": "one row per (class, client_id)",
        "gamma_raw": "raw Dirichlet gamma draw (positive)",
        "p": "normalized probability gamma_raw/sum(gamma_raw) for that class",
        "allocated_count": "Hamilton allocation of per_class_train samples for that class using p"
      },
      "numeric_precision": {
        "gamma_raw": "full_precision",
        "p": "full_precision",
        "allocated_count": "integer"
      }
    },
    "partition_csv": {
      "path": "partition_summary.csv",
      "columns": [
        "client_id",
        "is_byzantine",
        "n_train",
        "label_e
…[truncated]

#!/usr/bin/env Rscript

suppressPackageStartupMessages({
  library(ggplot2)
  library(gridExtra)
  library(jsonlite)
})

`%||%` <- function(x, y) if (!is.null(x) && !is.na(x) && nzchar(as.character(x))) x else y

sigmoid <- function(x) 1.0 / (1.0 + exp(-x))

safe_dir_create <- function(path) {
  if (!dir.exists(path)) dir.create(path, recursive = TRUE, showWarnings = FALSE)
}

read_bias_csv <- function(path, n_clients, n_classes) {
  if (is.null(path) || !nzchar(path) || !file.exists(path)) {
    return(NULL)
  }
  df <- read.csv(path, stringsAsFactors = FALSE)
  req <- c("client_id", sprintf("class_%d_weight", 0:(n_classes - 1)))
  if (!all(req %in% names(df))) {
    stop("bias csv missing required columns")
  }
  df <- df[order(df$client_id), ]
  if (nrow(df) != n_clients || any(df$client_id != 0:(n_clients - 1))) {
    stop("bias csv must have exactly one row per client")
  }
  mat <- as.matrix(df[, req[-1], drop = FALSE])
  storage.mode(mat) <- "double"
  col_sums <- colSums(mat)
  col_sums[col_sums <= 0] <- 1
  sweep(mat, 2, col_sums, "/")
}

hamilton_alloc <- function(p, total) {
  p <- as.numeric(p)
  p <- p / sum(p)
  raw <- p * total
  alloc <- as.integer(round(raw))
  gap <- as.integer(total - sum(alloc))
  if (gap > 0) {
    ord <- order(raw - alloc, decreasing = TRUE)
    alloc[ord[seq_len(gap)]] <- alloc[ord[seq_len(gap)]] + 1L
  } else if (gap < 0) {
    ord <- order(raw - alloc, decreasing = FALSE)
    take <- ord[seq_len(abs(gap))]
    alloc[take] <- pmax(0L, alloc[take] - 1L)
  }
  as.integer(alloc)
}

repair_partition <- function(partition, min_client_samples) {
  n_train <- rowSums(partition)
  while (min(n_train) < min_client_samples) {
    low <- which.min(n_train)
    high <- which.max(n_train)
    donor_class <- which.max(partition[high, ])
    if (partition[high, donor_class] <= 0) break
    partition[high, donor_class] <- partition[high, donor_class] - 1L
    partition[low, donor_class] <- partition[low, donor_class] + 1L
    n_train <- rowSums(partition)
  }
  partition
}

dirichlet_partition <- function(n_clients, n_classes, per_class_total, alpha, min_client_samples) {
  part <- matrix(0L, nrow = n_clients, ncol = n_classes)
  gammas <- matrix(0.0, nrow = n_clients, ncol = n_classes)
  for (k in seq_len(n_classes)) {
    g <- rgamma(n_clients, shape = alpha, rate = 1)
    gammas[, k] <- g
    part[, k] <- hamilton_alloc(g, per_class_total)
  }
  part <- repair_partition(part, min_client_samples)
  list(partition = part, gammas = gammas)
}

shannon_entropy <- function(counts) {
  s <- sum(counts)
  if (s <= 0) return(0)
  p <- counts / s
  p <- p[p > 0]
  -sum(p * log10(p))
}

row_trimmed_mean <- function(mat, trim_k) {
  keep <- order(apply(mat, 1, function(x) sqrt(sum(x * x))))[(trim_k + 1):(nrow(mat) - trim_k)]
  colMeans(mat[keep, , drop = FALSE])
}

run_sim <- function() {
  task_variant <- Sys.getenv("TASK_VARIANT", "public")
  seed <- as.integer(Sys.getenv("FEDSIMG_SEED", "0"))
  set.seed(seed)

  output_dir <- Sys.getenv("FEDSIMG_OUTPUT_DIR", "outputs")
  manifest_path <- Sys.getenv("FEDSIMG_MANIFEST_PATH", "/app/dataset_manifest.json")
  safe_dir_create(output_dir)

  manifest <- fromJSON(manifest_path, simplifyVector = TRUE)

  n_clients <- as.integer(manifest$simulation$n_clients)
  n_byz <- as.integer(manifest$simulation$n_byzantine)
  clients_per_round <- as.integer(manifest$simulation$clients_per_round)
  n_rounds <- as.integer(manifest$simulation$n_rounds)
  param_dim <- as.integer(manifest$simulation$param_dim)
  n_classes <- as.integer(manifest$dataset$n_classes)
  per_class <- as.integer(manifest$dataset$per_class_train)

  alpha <- as.numeric(manifest$partition$alpha_default)
  noise_scale <- as.numeric(manifest$attack$noise_scale_default)
  signal_scale <- as.numeric(Sys.getenv("FEDSIMG_SIGNAL_SCALE", "0.12"))
  trim_k <- as.integer(manifest$simulation$trimmed_mean$trim_k_default)
  bias_strength <- as.numeric(manifest$client_bias$bias_strength_default)

  bias_csv <- Sys.getenv(m
…[truncated]

client_id,class_0_weight,class_1_weight,class_2_weight,class_3_weight,class_4_weight,class_5_weight,class_6_weight,class_7_weight,class_8_weight,class_9_weight
0,0.28,0.10,0.07,0.06,0.06,0.06,0.07,0.08,0.12,0.10
1,0.05,0.06,0.09,0.14,0.15,0.14,0.12,0.10,0.08,0.07
2,0.08,0.08,0.08,0.08,0.08,0.08,0.12,0.14,0.14,0.12
3,0.04,0.05,0.06,0.07,0.10,0.13,0.15,0.15,0.14,0.11
4,0.16,0.14,0.12,0.10,0.09,0.08,0.07,0.06,0.10,0.08

hidden
public
---
/usr/bin/Rscript

/app/outputs/hidden:

/app/outputs/public:

Existing file was successfully overwritten at /app/analysis.R

<system>Tool ran without output or errors</system>

total 1952
drwxrws--- 2 h2tagent h2tagent    4096 May 28 23:09 .
drwxrws--- 4 h2tagent h2tagent    4096 May 28 19:31 ..
-rw-r--r-- 1 h2tagent h2tagent    1016 May 28 23:09 analysis_summary.json
-rw-r--r-- 1 h2tagent h2tagent   12612 May 28 23:09 byzantine_audit.csv
-rw-r--r-- 1 h2tagent h2tagent  731196 May 28 23:09 client_updates.csv
-rw-r--r-- 1 h2tagent h2tagent 1115104 May 28 23:09 client_updates_clean.csv
-rw-r--r-- 1 h2tagent h2tagent   74421 May 28 23:09 diagnostics.png
-rw-r--r-- 1 h2tagent h2tagent   45882 May 28 23:09 dirichlet_gammas.csv
-rw-r--r-- 1 h2tagent h2tagent    1737 May 28 23:09 metrics_by_round.csv
-rw-r--r-- 1 h2tagent h2tagent    3363 May 28 23:09 partition_matrix.csv
-rw-r--r-- 1 h2tagent h2tagent    2952 May 28 23:09 partition_summary.csv

"round","method","accuracy","cosine_sim","update_norm","byzantine_selected","selected_clients"
0,"fedavg",0.701539,0.44856,4.016918,1,"80 90 62 55 94 67 31 56 60 65 10 19 40 14 61 57 7 68 75 51"
0,"trimmed_mean",0.946524,0.727073,2.572666,1,"80 90 62 55 94 67 31 56 60 65 10 19 40 14 61 57 7 68 75 51"
1,"fedavg",0.772213,0.496612,3.54306,2,"90 32 13 64 47 53 95 42 35 72 6 1 30 78 74 55 19 36 15 38"
1,"trimmed_mean",0.945395,0.72637,2.607558,2,"90 32 13 64 47 53 95 42 35 72 6 1 30 78 74 55 19 36 15 38"
---
"round","client_id","is_byzantine","is_selected","client_weight","update_norm"
0,0,1,0,0,0
0,1,1,0,0,0
0,2,1,0,0,0
0,3,1,0,0,0
---
"round","client_id","coord","update_value"
0,7,0,-1.571876
0,7,1,0.292317
---
"round","client_id","coord","u_clean","u_submitted"
0,7,0,0.156615,-1.571876
0,7,1,0.261323,0.292317
---
"client_id","class_0_count","class_1_count","class_2_count","class_3_count","class_4_count","class_5_count","class_6_count","class_7_count","class_8_count","class_9_count"
0,99,20,180,32,26,72,46,229,3,1
1,38,2,29,0,5,3,311,291,71,6
---
"client_id","is_byzantine","n_train","label_entropy","top_label","top_label_share"
0,1,708,1.792905,7,0.323446
1,1,756,1.339652,6,0.411376
2,1,759,1.427729,1,0.466403
3,1,640,1.563247,3,0.335938
---
"class","client_id","gamma_raw","p","allocated_count"
0,0,0.760516834810734,0.0165288490248016,99
0,1,0.293697522769724,0.00638313550814045,38
0,2,1.86634352361331,0.0405625607721065,243
0,3,1.48173928793115,0.0322036855244973,193

{
  "task_variant": "public",
  "seed": 42,
  "env": {
    "FEDSIMG_ALPHA": 0.5,
    "FEDSIMG_NOISE_SCALE": 2,
    "FEDSIMG_SIGNAL_SCALE": 0.12,
    "FEDSIMG_TRIM_K": 2,
    "FEDSIMG_BIAS_STRENGTH": 0.35,
    "FEDSIMG_CLIENT_BIAS_CSV": "/app/example_client_bias.csv"
  },
  "simulation_meta": {
    "n_clients": 100,
    "n_byzantine": 10,
    "clients_per_round": 20,
    "n_rounds": 8,
    "param_dim": 256,
    "trim_k": 2
  },
  "byzantine_client_ids": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
  "final_metrics": {
    "trimmed_mean_accuracy": 0.960252,
    "fedavg_accuracy": 0.92827,
    "delta": 0.031982
  },
  "provenance": {
    "manifest_md5": "475adf00a65314160823670bdaef093c",
    "selection_histogram": [2, 1, 1, 3, 1, 3, 1, 1, 0, 2, 2, 2, 3, 2, 3, 2, 0, 1, 1, 3, 1, 3, 0, 0, 2, 2, 0, 0, 0, 0, 1, 3, 2, 2, 1, 3, 3, 2, 3, 2, 2, 3, 2, 1, 2, 1, 2, 2, 3, 2, 2, 2, 0, 1, 2, 3, 2, 1, 1, 3, 3, 2, 2, 2, 3, 1, 2, 1, 1, 1, 2, 1, 1, 0, 1, 4, 2, 1, 1, 0, 3, 1, 1, 1, 0, 0, 2, 0, 2, 2, 3, 0, 2, 1, 2, 3, 3, 1, 1, 0]
  }
}

metrics CSV final fed.accuracy : 0.92827
metrics CSV final tm.accuracy  : 0.960252
JSON final_metrics: {'trimmed_mean_accuracy': 0.960252, 'fedavg_accuracy': 0.92827, 'delta': 0.031982}
delta expected: 0.031981999999999955
selection_histogram length: 100
sum selection_histogram    : 160
expected total (8 rounds * 20): 160

Metrics rows: 16 (expected 16)
Partition matrix rows: 100 (expected 100)
All classes sum to 6000 ✓
Dirichlet gammas rows: 1000 (expected 10*100=1000)
Partition summary rows: 100 (expected 100)
Byzantine audit rows: 800 (expected 800)
Client updates rows: 40960 (expected 8*20*256=40960)
Client updates clean rows: 40960 (expected 8*20*256=40960)

tm=0.960252, fed=0.92827, delta=0.031982
tm - fed = 0.031981999999999955
match? False

raw: 0.03198199999999995491
{"v":0.031982} 
{"v":0.031981999999999955}

raw: 0.96025199999999999445
{"v":0.96025199999999999} 
{"v":0.96025199999999999}

True
0.960252
0.960252

{"a":0.960252,"b":0.92827,"delta":0.031982}

{"a":0.96025199999999999,"b":0.92827000000000004,"delta":0.031981999999999955}

{"ints":[1,2,3]}

The file /app/analysis.R has been edited successfully.

{
  "task_variant": "public",
  "seed": 42,
  "env": {
    "FEDSIMG_ALPHA": 0.5,
    "FEDSIMG_NOISE_SCALE": 2,
    "FEDSIMG_SIGNAL_SCALE": 0.12,
    "FEDSIMG_TRIM_K": 2,
    "FEDSIMG_BIAS_STRENGTH": 0.34999999999999998,
    "FEDSIMG_CLIENT_BIAS_CSV": "/app/example_client_bias.csv"
  },
  "simulation_meta": {
    "n_clients": 100,
    "n_byzantine": 10,
    "clients_per_round": 20,
    "n_rounds": 8,
    "param_dim": 256,
    "trim_k": 2
  },
  "byzantine_client_ids": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
  "final_metrics": {
    "trimmed_mean_accuracy": 0.96025199999999999,
    "fedavg_accuracy": 0.92827000000000004,
    "delta": 0.031981999999999955
  },
  "provenance": {
    "manifest_md5": "475adf00a65314160823670bdaef093c",
    "selection_histogram": [2, 1, 1, 3, 1, 3, 1, 1, 0, 2, 2, 2, 3, 2, 3, 2, 0, 1, 1, 3, 1, 3, 0, 0, 2, 2, 0, 0, 0, 0, 1, 3, 2, 2, 1, 3, 3, 2, 3, 2, 2, 3, 2, 1, 2, 1, 2, 2, 3, 2, 2, 2, 0, 1, 2, 3, 2, 1, 1, 3, 3, 2, 2, 2, 3, 1, 2, 1, 1, 1, 2, 1, 1, 0, 1, 4, 2, 1, 1, 0, 3, 1, 1, 1, 0, 0, 2, 0, 2, 2, 3, 0, 2, 1, 2, 3, 3, 1, 1, 0]
  }
}

Values from JSON:
  FEDSIMG_ALPHA: 0.5
  FEDSIMG_NOISE_SCALE: 2
  FEDSIMG_SIGNAL_SCALE: 0.12
  FEDSIMG_TRIM_K: 2
  FEDSIMG_BIAS_STRENGTH: 0.35
  FEDSIMG_CLIENT_BIAS_CSV: '/app/example_client_bias.csv'

Numeric check:
tm=0.960252, fed=0.92827, delta=0.031981999999999955
tm - fed = 0.031981999999999955
delta == tm - fed: True

CSV fed accuracy: 0.92827
CSV tm accuracy:  0.960252
JSON matches CSV fed? True
JSON matches CSV tm?  True

Env parses correctly ✓
All clients have >= 20 samples (min=125) ✓
Byzantine client IDs are 0..9 ✓
Per-round selected client_weight sums to 1 ✓
Honest clients: u_clean == u_submitted (37120 rows) ✓
Byzantine clients: u_clean != u_submitted rows = 3840/3840
All rounds have exactly 20 selected clients ✓

All checks pass!

---
analysis_summary.json
byzantine_audit.csv
client_updates.csv
client_updates_clean.csv
diagnostics.png
dirichlet_gammas.csv
metrics_by_round.csv
partition_matrix.csv
partition_summary.csv
---
{
  "task_variant": "hidden",
  "seed": 7,
  "env": {
    "FEDSIMG_ALPHA": 0.29999999999999999,
    "FEDSIMG_NOISE_SCALE": 3,
    "FEDSIMG_SIGNAL_SCALE": 0.14999999999999999,
    "FEDSIMG_TRIM_K": 3,
    "FEDSIMG_BIAS_STRENGTH": 0.20000000000000001,
    "FEDSIMG_CLIENT_BIAS_CSV": ""
  },
  "simulation_meta": {
    "n_clients": 100,
    "n_byzantine": 10,
    "clients_per_round": 20,
    "n_rounds": 8,
    "param_dim": 256,
    "trim_k": 3
  },
  "byzantine_client_ids": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
  "final_metrics": {
    "trimmed_mean_accuracy": 0.98702400000000001,
    "fedavg_accuracy": 0.94867000000000001,
    "delta": 0.038353999999999999
  },

All env parses correctly ✓
selection_histogram length: 100 (need 100)
Selection histogram sums to 160 ✓
simulation_meta has all required keys ✓
Hidden variant ran OK!

metrics identical
gammas identical
summary identical
[stderr]
bash: file: command not found
bash: file: command not found

-rw-r--r-- 1 h2tagent h2tagent 76408 May 28 23:11 /app/outputs/hidden/diagnostics.png
-rw-r--r-- 1 h2tagent h2tagent 74421 May 28 23:11 /app/outputs/public/diagnostics.png
[stderr]
bash: xxd: command not found

signature match: True
width x height = 1400 x 900

<system>Image resized from 1400x900 to 1372x882 to fit display limits</system>
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…[truncated]

All 9 required outputs present ✓
All CSV schemas exact ✓
All sort orders correct ✓
All JSON keys satisfied ✓
Manifest md5 correct: 475adf00a65314160823670bdaef093c ✓

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ALL VERIFICATIONS PASSED
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