Agent Conversation: anthropic/claude-opus-4.6
Model: anthropic/claude-opus-4.6
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Messages: 18
UserStep 0
Task: ml-federated-aggregation
Federated Learning Aggregation Strategy Design
Research Question
Design a novel server-side aggregation strategy for federated learning that achieves better convergence and higher test accuracy under heterogeneous (non-IID) data distributions across clients.
Background
Federated Learning (FL) trains a shared global model across many clients without centralizing data. The canonical algorithm, FedAvg, simply averages client model parameters weighted by sample count. However, when client data distributions are heterogeneous (non-IID), FedAvg suffers from "client drift" where local updates diverge, leading to slow convergence or poor final accuracy. Research has produced several improvements: FedProx adds a proximal penalty to local objectives, SCAFFOLD uses control variates for variance reduction, and methods like FedNova normalize updates by local steps. The aggregation strategy — how the server combines client updates into the global model — is the core algorithmic component that determines convergence behavior.
Task
Modify the ServerAggregator class in custom_fl_aggregation.py. You must implement the aggregate() method that takes the current global model state, a list of client updates (model parameters + metadata), and returns the new global model state. You may also customize client selection via select_clients().
Interface
class ServerAggregator:
def __init__(self, global_model, args):
# Initialize aggregation state (momentum buffers, control variates, etc.)
def aggregate(self, global_state_dict, client_updates, round_num):
# global_state_dict: OrderedDict of current global model parameters
# client_updates: list of (state_dict, num_samples, avg_loss) tuples
# round_num: current communication round (0-indexed)
# Returns: OrderedDict of updated global model parameters
def select_clients(self, num_available, num_to_select, round_num):
# Returns: list of client indices to participate this roundEvaluation
The aggregation strategy is evaluated on three benchmarks with non-IID data:
- CIFAR-10 with Dirichlet split (alpha=0.1) — 100 clients, image classification
- FEMNIST (EMNIST ByClass) with Dirichlet split — 100 clients, character recognition
- Shakespeare (next character prediction) — naturally non-IID by speaker
Metric: test accuracy after 200 communication rounds (higher is better). Each round, 10 of 100 clients are selected, each trains for 5 local epochs with SGD (lr=0.01).
flower/custom_fl_aggregation.py [EDITABLE — lines 353–424 only]
1: # Custom federated learning aggregation strategy for MLS-Bench
2: #
3: # EDITABLE section: ServerAggregator class (aggregate method + helpers).
4: # FIXED sections: everything else (config, data partitioning, client training,
5: # FL simulation loop, evaluation).
6: import argparse
AssistantStep 1edit
Tool Input
1{
2 "op": "replace",
3 "filename": "flower/custom_fl_aggregation.py",
4 "start_line": 353,
5 "end_line": 425,
6 "content": "class ServerAggregator:\n \"\"\"CGMA — Consensus-Guided Momentum Aggregation.\n\n A novel federated aggregation strategy that addresses non-IID client drift\n through three synergistic mechanisms:\n\n 1. **Adaptive Update Clipping**: Clips client pseudo-gradients whose norm\n exceeds a data-driven threshold (multiple of the median norm), providing\n robustness against outlier updates from highly skewed local distributions.\n\n 2. **Consensus-Guided Reweighting**: Maintains an EMA of past aggregated\n update directions as a \"consensus trajectory\". Each round, client updates\n are reweighted by their cosine similarity with this trajectory — clients\n aligned with the historical optimization direction get higher weight,\n while contradictory updates are dampened. Using historical EMA (not\n current-round average) provides an independent, stable reference.\n\n 3. **Polyak Heavy-Ball Momentum**: Adds a fraction of the previous server\n step to the current update, smoothing the trajectory and reducing\n variance from stochastic client sampling (10% participation per round).\n\n The combination is novel: clipping handles magnitude outliers, consensus\n reweighting handles directional outliers, and momentum provides temporal\n smoothing — each addressing a different variance source in non-IID FL.\n \"\"\"\n\n def __init__(self, global_model, args):\n self.args = args\n # Heavy-ball momentum\n self.prev_global_state = None\n self.beta_momentum = 0.5\n # EMA of update directions for consensus reference\n self.ema_direction = None\n self.beta_ema = 0.7\n # Consensus reweighting strength\n self.consensus_strength = 0.5\n # Adaptive clipping factor (multiple of median norm)\n self.clip_factor = 2.0\n\n def aggregate(self, global_state_dict, client_updates, round_num):\n n_clients = len(client_updates)\n total_samples = sum(n for _, n, _ in client_updates)\n keys = list(global_state_dict.keys())\n\n # --- 1. Compute per-client deltas (pseudo-gradients) ---\n client_deltas = []\n for state_dict, n_samples, avg_loss in client_updates:\n delta = OrderedDict()\n for key in keys:\n delta[key] = state_dict[key].float() - global_state_dict[key].float()\n client_deltas.append(delta)\n\n # --- 2. Adaptive clipping based on median norm ---\n norms = []\n for d in client_deltas:\n norm_sq = sum(d[k].pow(2).sum().item() for k in keys)\n norms.append(norm_sq ** 0.5)\n\n sorted_norms = sorted(norms)\n median_norm = sorted_norms[n_clients // 2]\n threshold = self.clip_factor * max(median_norm, 1e-8)\n\n for i in range(n_clients):\n if norms[i] > threshold:\n scale = threshold / norms[i]\n for k in keys:\n client_deltas[i][k] *= scale\n\n # --- 3. Consensus-guided reweighting ---\n sample_weights = [client_updates[i][1] / total_samples\n for i in range(n_clients)]\n\n if self.ema_direction is not None:\n # Cosine similarity of each client delta with EMA direction\n ema_norm_sq = sum(\n self.ema_direction[k].pow(2).sum().item() for k in keys)\n ema_norm = max(ema_norm_sq ** 0.5, 1e-8)\n\n sims = []\n for i in range(n_clients):\n dot = sum((client_deltas[i][k] * self.ema_direction[k]).sum().item()\n for k in keys)\n c_norm = sum(client_deltas[i][k].pow(2).sum().item()\n for k in keys) ** 0.5\n c_norm = max(c_norm, 1e-8)\n sims.append(dot / (c_norm * ema_norm))\n\n # Boost aligned clients, leave others at baseline\n weights = []\n for i in range(n_clients):\n w = sample_weights[i] * (\n 1.0 + self.consensus_strength * max(sims[i], 0.0))\n weights.append(w)\n w_sum = sum(weights)\n weights = [w / w_sum for w in weights]\n else:\n weights = sample_weights\n\n # --- 4. Weighted aggregation of deltas ---\n avg_delta = OrderedDict()\n for key in keys:\n avg_delta[key] = sum(\n client_deltas[i][key] * weights[i] for i in range(n_clients))\n\n # --- 5. Update EMA direction for future consensus ---\n if self.ema_direction is None:\n self.ema_direction = OrderedDict()\n for k in keys:\n self.ema_direction[k] = avg_delta[k].clone()\n else:\n for k in keys:\n self.ema_direction[k] = (self.beta_ema * self.ema_direction[k]\n + (1 - self.beta_ema) * avg_delta[k])\n\n # --- 6. Polyak heavy-ball momentum ---\n if self.prev_global_state is not None:\n momentum = OrderedDict()\n for k in keys:\n momentum[k] = self.beta_momentum * (\n global_state_dict[k].float()\n - self.prev_global_state[k].float())\n else:\n momentum = {k: torch.zeros_like(global_state_dict[k].float())\n for k in keys}\n\n # Store current state before update\n self.prev_global_state = OrderedDict()\n for k in keys:\n self.prev_global_state[k] = global_state_dict[k].clone()\n\n # --- 7. Apply: θ_{t+1} = θ_t + Δ_t + β*(θ_t − θ_{t-1}) ---\n new_state = OrderedDict()\n for key in keys:\n updated = (global_state_dict[key].float()\n + avg_delta[key] + momentum[key])\n new_state[key] = updated.to(global_state_dict[key].dtype)\n\n return new_state\n\n def select_clients(self, num_available, num_to_select, round_num):\n return random.sample(range(num_available),\n min(num_to_select, num_available))\n"
7}Tool ResultStep 1
ERROR: Lines 353..425 exceed the editable range. You may only edit lines 353–424.
[Current file: flower/custom_fl_aggregation.py | editable: 353–424 | total: 521 lines]
353 class ServerAggregator:
354 """Server-side aggregation strategy for federated learning.
355
... (66 more lines) ...
422 list of client indices to participate
423 """
424 return random.sample(range(num_available), min(num_to_select, num_available))AssistantStep 2edit
Tool Input
1{
2 "op": "replace",
3 "filename": "flower/custom_fl_aggregation.py",
4 "start_line": 353,
5 "end_line": 424,
6 "content": "class ServerAggregator:\n \"\"\"CGMA — Consensus-Guided Momentum Aggregation.\n Novel aggregation: adaptive clipping + consensus reweighting via EMA\n trajectory + Polyak heavy-ball momentum for non-IID FL.\n \"\"\"\n\n def __init__(self, global_model, args):\n self.args = args\n self.prev_global_state = None\n self.beta_momentum = 0.5\n self.ema_direction = None\n self.beta_ema = 0.7\n self.consensus_strength = 0.5\n self.clip_factor = 2.0\n\n def aggregate(self, global_state_dict, client_updates, round_num):\n n_clients = len(client_updates)\n total_samples = sum(n for _, n, _ in client_updates)\n keys = list(global_state_dict.keys())\n\n # 1. Per-client deltas\n client_deltas = []\n for state_dict, n_samples, avg_loss in client_updates:\n delta = OrderedDict()\n for key in keys:\n delta[key] = state_dict[key].float() - global_state_dict[key].float()\n client_deltas.append(delta)\n\n # 2. Adaptive clipping (median-based threshold)\n norms = []\n for d in client_deltas:\n norms.append(sum(d[k].pow(2).sum().item() for k in keys) ** 0.5)\n median_norm = sorted(norms)[n_clients // 2]\n threshold = self.clip_factor * max(median_norm, 1e-8)\n for i in range(n_clients):\n if norms[i] > threshold:\n s = threshold / norms[i]\n for k in keys:\n client_deltas[i][k] *= s\n\n # 3. Consensus-guided reweighting\n sw = [client_updates[i][1] / total_samples for i in range(n_clients)]\n if self.ema_direction is not None:\n en = max(sum(self.ema_direction[k].pow(2).sum().item() for k in keys) ** 0.5, 1e-8)\n sims = []\n for i in range(n_clients):\n dot = sum((client_deltas[i][k] * self.ema_direction[k]).sum().item() for k in keys)\n cn = max(sum(client_deltas[i][k].pow(2).sum().item() for k in keys) ** 0.5, 1e-8)\n sims.append(dot / (cn * en))\n weights = [sw[i] * (1.0 + self.consensus_strength * max(sims[i], 0.0)) for i in range(n_clients)]\n ws = sum(weights)\n weights = [w / ws for w in weights]\n else:\n weights = sw\n\n # 4. Weighted aggregation\n avg_delta = OrderedDict()\n for key in keys:\n avg_delta[key] = sum(client_deltas[i][key] * weights[i] for i in range(n_clients))\n\n # 5. Update EMA direction\n if self.ema_direction is None:\n self.ema_direction = OrderedDict()\n for k in keys:\n self.ema_direction[k] = avg_delta[k].clone()\n else:\n for k in keys:\n self.ema_direction[k] = self.beta_ema * self.ema_direction[k] + (1 - self.beta_ema) * avg_delta[k]\n\n # 6. Polyak heavy-ball momentum\n if self.prev_global_state is not None:\n mom = {k: self.beta_momentum * (global_state_dict[k].float() - self.prev_global_state[k].float()) for k in keys}\n else:\n mom = {k: torch.zeros_like(global_state_dict[k].float()) for k in keys}\n self.prev_global_state = OrderedDict((k, global_state_dict[k].clone()) for k in keys)\n\n # 7. Apply: θ_{t+1} = θ_t + Δ_t + β*(θ_t − θ_{t-1})\n new_state = OrderedDict()\n for key in keys:\n new_state[key] = (global_state_dict[key].float() + avg_delta[key] + mom[key]).to(global_state_dict[key].dtype)\n return new_state\n\n def select_clients(self, num_available, num_to_select, round_num):\n return random.sample(range(num_available), min(num_to_select, num_available))"
7}Tool ResultStep 2
OK: Replaced lines 353..424 with 84 line(s) in flower/custom_fl_aggregation.py. Editable range: 353–436.
[Current file: flower/custom_fl_aggregation.py | editable: 353–436 | total: 533 lines]
353 class ServerAggregator:
354 """CGMA — Consensus-Guided Momentum Aggregation.
355 Novel aggregation: adaptive clipping + consensus reweighting via EMA
... (78 more lines) ...
434
435 def select_clients(self, num_available, num_to_select, round_num):
436 return random.sample(range(num_available), min(num_to_select, num_available))AssistantStep 3test
Tool ResultStep 3
[Test #1] (2 tests remaining; call submit(n=N) to choose which test result to submit as final) ### cifar10 (scripts/cifar10.sh) Training metrics (last rounds): TRAIN_METRICS round=180 avg_loss=0.0961 round_time=3.4s TRAIN_METRICS round=185 avg_loss=0.1110 round_time=4.1s TRAIN_METRICS round=190 avg_loss=0.0339 round_time=3.2s TRAIN_METRICS round=195 avg_loss=0.0282 round_time=3.8s TRAIN_METRICS round=200 avg_loss=0.0489 round_time=3.4s Evaluation progress: EVAL round=160 test_loss=1.6712 test_accuracy=0.6418 elapsed=593s EVAL round=170 test_loss=1.8197 test_accuracy=0.6157 elapsed=636s EVAL round=180 test_loss=1.6126 test_accuracy=0.6477 elapsed=668s EVAL round=190 test_loss=1.5527 test_accuracy=0.6687 elapsed=708s EVAL round=200 test_loss=1.7011 test_accuracy=0.6543 elapsed=746s Final metrics (cifar10): test_accuracy_cifar10: 0.6543 test_loss_cifar10: 1.7011 best_accuracy_cifar10: 0.6687 ### femnist (scripts/femnist.sh) Training metrics (last rounds): TRAIN_METRICS round=180 avg_los
AssistantStep 4edit
Tool Input
1{
2 "op": "replace",
3 "filename": "flower/custom_fl_aggregation.py",
4 "start_line": 353,
5 "end_line": 436,
6 "content": "class ServerAggregator:\n \"\"\"CGMA — Consensus-Guided Momentum Aggregation with Adaptive Decay.\n Novel aggregation combining:\n 1. Adaptive median-based clipping for outlier robustness\n 2. Consensus reweighting via EMA trajectory alignment\n 3. Heavy-ball momentum with warm-up and decay\n \"\"\"\n\n def __init__(self, global_model, args):\n self.args = args\n self.prev_global_state = None\n self.ema_direction = None\n self.beta_ema = 0.8\n self.consensus_strength = 0.5\n self.clip_factor = 2.5\n # Momentum schedule: warm up then hold\n self.beta_momentum_max = 0.4\n self.momentum_warmup_rounds = 10\n # Server learning rate > 1 can sometimes help\n self.server_lr = 1.0\n\n def aggregate(self, global_state_dict, client_updates, round_num):\n n_clients = len(client_updates)\n total_samples = sum(n for _, n, _ in client_updates)\n keys = list(global_state_dict.keys())\n\n # Momentum schedule: linear warm-up\n if round_num < self.momentum_warmup_rounds:\n beta_m = self.beta_momentum_max * (round_num / self.momentum_warmup_rounds)\n else:\n beta_m = self.beta_momentum_max\n\n # 1. Per-client deltas\n client_deltas = []\n for state_dict, n_samples, avg_loss in client_updates:\n delta = OrderedDict()\n for key in keys:\n delta[key] = state_dict[key].float() - global_state_dict[key].float()\n client_deltas.append(delta)\n\n # 2. Adaptive clipping (median-based threshold)\n norms = []\n for d in client_deltas:\n norms.append(sum(d[k].pow(2).sum().item() for k in keys) ** 0.5)\n median_norm = sorted(norms)[n_clients // 2]\n threshold = self.clip_factor * max(median_norm, 1e-8)\n for i in range(n_clients):\n if norms[i] > threshold:\n s = threshold / norms[i]\n for k in keys:\n client_deltas[i][k] *= s\n\n # 3. Consensus-guided reweighting using EMA trajectory\n sw = [client_updates[i][1] / total_samples for i in range(n_clients)]\n if self.ema_direction is not None and round_num >= 3:\n en = max(sum(self.ema_direction[k].pow(2).sum().item() for k in keys) ** 0.5, 1e-8)\n sims = []\n for i in range(n_clients):\n dot = sum((client_deltas[i][k] * self.ema_direction[k]).sum().item() for k in keys)\n cn = max(sum(client_deltas[i][k].pow(2).sum().item() for k in keys) ** 0.5, 1e-8)\n sims.append(dot / (cn * en))\n # Soft reweighting: boost aligned, slightly dampen misaligned\n weights = []\n for i in range(n_clients):\n # Use sigmoid-like mapping: max(sim, -0.5) to avoid completely zeroing out\n sim_clamped = max(sims[i], -0.3)\n w = sw[i] * (1.0 + self.consensus_strength * sim_clamped)\n weights.append(max(w, 1e-8))\n ws = sum(weights)\n weights = [w / ws for w in weights]\n else:\n weights = sw\n\n # 4. Weighted aggregation\n avg_delta = OrderedDict()\n for key in keys:\n avg_delta[key] = sum(client_deltas[i][key] * weights[i] for i in range(n_clients))\n\n # 5. Update EMA direction\n if self.ema_direction is None:\n self.ema_direction = OrderedDict()\n for k in keys:\n self.ema_direction[k] = avg_delta[k].clone()\n else:\n for k in keys:\n self.ema_direction[k] = self.beta_ema * self.ema_direction[k] + (1 - self.beta_ema) * avg_delta[k]\n\n # 6. Polyak heavy-ball momentum\n if self.prev_global_state is not None:\n mom = {k: beta_m * (global_state_dict[k].float() - self.prev_global_state[k].float()) for k in keys}\n else:\n mom = {k: torch.zeros_like(global_state_dict[k].float()) for k in keys}\n self.prev_global_state = OrderedDict((k, global_state_dict[k].clone()) for k in keys)\n\n # 7. Apply: θ_{t+1} = θ_t + lr * Δ_t + β*(θ_t − θ_{t-1})\n new_state = OrderedDict()\n for key in keys:\n new_state[key] = (global_state_dict[key].float() + self.server_lr * avg_delta[key] + mom[key]).to(global_state_dict[key].dtype)\n return new_state\n\n def select_clients(self, num_available, num_to_select, round_num):\n return random.sample(range(num_available), min(num_to_select, num_available))"
7}Tool ResultStep 4
OK: Replaced lines 353..436 with 102 line(s) in flower/custom_fl_aggregation.py. Editable range: 353–454.
[Current file: flower/custom_fl_aggregation.py | editable: 353–454 | total: 551 lines]
353 class ServerAggregator:
354 """CGMA — Consensus-Guided Momentum Aggregation with Adaptive Decay.
355 Novel aggregation combining:
... (96 more lines) ...
452
453 def select_clients(self, num_available, num_to_select, round_num):
454 return random.sample(range(num_available), min(num_to_select, num_available))AssistantStep 5test
Tool ResultStep 5
[Test #2] (1 test remaining; call submit(n=N) to choose which test result to submit as final) ### cifar10 (scripts/cifar10.sh) Training metrics (last rounds): TRAIN_METRICS round=180 avg_loss=0.0919 round_time=4.9s TRAIN_METRICS round=185 avg_loss=0.0981 round_time=5.2s TRAIN_METRICS round=190 avg_loss=0.0359 round_time=4.4s TRAIN_METRICS round=195 avg_loss=0.0388 round_time=5.2s TRAIN_METRICS round=200 avg_loss=0.0662 round_time=4.5s Evaluation progress: EVAL round=160 test_loss=1.5424 test_accuracy=0.6323 elapsed=710s EVAL round=170 test_loss=1.6133 test_accuracy=0.6285 elapsed=765s EVAL round=180 test_loss=1.5337 test_accuracy=0.6385 elapsed=810s EVAL round=190 test_loss=1.3986 test_accuracy=0.6731 elapsed=859s EVAL round=200 test_loss=1.6660 test_accuracy=0.6411 elapsed=910s Final metrics (cifar10): test_accuracy_cifar10: 0.6411 test_loss_cifar10: 1.6660 best_accuracy_cifar10: 0.6731 ### femnist (scripts/femnist.sh) Training metrics (last rounds): TRAIN_METRICS round=180 avg_loss
AssistantStep 6edit
Tool Input
1{
2 "op": "replace",
3 "filename": "flower/custom_fl_aggregation.py",
4 "start_line": 353,
5 "end_line": 454,
6 "content": "class ServerAggregator:\n \"\"\"CGMA — Consensus-Guided Momentum Aggregation.\n Combines: (1) adaptive norm clipping, (2) lightweight consensus reweighting\n via flattened vectors, (3) heavy-ball momentum with warm-up.\n \"\"\"\n\n def __init__(self, global_model, args):\n self.args = args\n self.prev_global_state = None\n self.ema_flat = None # flattened EMA direction\n self.beta_ema = 0.8\n self.consensus_strength = 0.5\n self.clip_factor = 2.5\n self.beta_momentum_max = 0.4\n self.momentum_warmup = 10\n self.keys = None\n\n def _flatten(self, delta):\n return torch.cat([delta[k].reshape(-1) for k in self.keys])\n\n def aggregate(self, global_state_dict, client_updates, round_num):\n n = len(client_updates)\n total_samples = sum(ns for _, ns, _ in client_updates)\n if self.keys is None:\n self.keys = list(global_state_dict.keys())\n keys = self.keys\n\n # Momentum warm-up\n beta_m = min(self.beta_momentum_max, self.beta_momentum_max * round_num / max(self.momentum_warmup, 1))\n\n # 1. Per-client flat deltas\n flat_deltas = []\n for sd, ns, al in client_updates:\n parts = []\n for k in keys:\n parts.append((sd[k].float() - global_state_dict[k].float()).reshape(-1))\n flat_deltas.append(torch.cat(parts))\n\n # 2. Adaptive clipping\n norms = [fd.norm().item() for fd in flat_deltas]\n median_norm = sorted(norms)[n // 2]\n threshold = self.clip_factor * max(median_norm, 1e-8)\n for i in range(n):\n if norms[i] > threshold:\n flat_deltas[i] *= (threshold / norms[i])\n\n # 3. Consensus reweighting\n sw = [client_updates[i][1] / total_samples for i in range(n)]\n if self.ema_flat is not None and round_num >= 3:\n ema_n = max(self.ema_flat.norm().item(), 1e-8)\n sims = []\n for i in range(n):\n cn = max(flat_deltas[i].norm().item(), 1e-8)\n dot = torch.dot(flat_deltas[i], self.ema_flat).item()\n sims.append(dot / (cn * ema_n))\n weights = []\n for i in range(n):\n sim_c = max(sims[i], -0.3)\n w = sw[i] * (1.0 + self.consensus_strength * sim_c)\n weights.append(max(w, 1e-8))\n ws = sum(weights)\n weights = [w / ws for w in weights]\n else:\n weights = sw\n\n # 4. Weighted average delta (flat)\n avg_flat = torch.zeros_like(flat_deltas[0])\n for i in range(n):\n avg_flat += flat_deltas[i] * weights[i]\n\n # 5. Update EMA\n if self.ema_flat is None:\n self.ema_flat = avg_flat.clone()\n else:\n self.ema_flat = self.beta_ema * self.ema_flat + (1 - self.beta_ema) * avg_flat\n\n # 6. Unflatten avg_delta\n avg_delta = OrderedDict()\n offset = 0\n for k in keys:\n shape = global_state_dict[k].shape\n numel = global_state_dict[k].numel()\n avg_delta[k] = avg_flat[offset:offset+numel].reshape(shape)\n offset += numel\n\n # 7. Momentum\n if self.prev_global_state is not None:\n mom = OrderedDict()\n for k in keys:\n mom[k] = beta_m * (global_state_dict[k].float() - self.prev_global_state[k].float())\n else:\n mom = {k: torch.zeros_like(global_state_dict[k].float()) for k in keys}\n self.prev_global_state = OrderedDict((k, global_state_dict[k].clone()) for k in keys)\n\n # 8. Apply update\n new_state = OrderedDict()\n for k in keys:\n new_state[k] = (global_state_dict[k].float() + avg_delta[k] + mom[k]).to(global_state_dict[k].dtype)\n return new_state\n\n def select_clients(self, num_available, num_to_select, round_num):\n return random.sample(range(num_available), min(num_to_select, num_available))"
7}Tool ResultStep 6
OK: Replaced lines 353..454 with 102 line(s) in flower/custom_fl_aggregation.py. Editable range: 353–454.
[Current file: flower/custom_fl_aggregation.py | editable: 353–454 | total: 551 lines]
353 class ServerAggregator:
354 """CGMA — Consensus-Guided Momentum Aggregation.
355 Combines: (1) adaptive norm clipping, (2) lightweight consensus reweighting
... (96 more lines) ...
452
453 def select_clients(self, num_available, num_to_select, round_num):
454 return random.sample(range(num_available), min(num_to_select, num_available))AssistantStep 7test
Tool ResultStep 7
[Test #3] (0 tests remaining; call submit(n=N) to choose which test result to submit as final)
[NOTE] This was your last test. You MUST now call submit(n=X) to choose which test result to submit as your final answer.
### cifar10 (scripts/cifar10.sh)
[COMMAND FAILED exit=1]
Device: cuda
Loading dataset: cifar10
Number of clients: 100
Traceback (most recent call last):
File "/srv/home/bohanlyu/MLS-Bench/vendor/workspace/ml-federated-aggregation/anthropic_claude-opus-4.6_20260414_110948/flower/custom_fl_aggregation.py", line 551, in <module>
run_fl_simulation(args)
File "/srv/home/bohanlyu/MLS-Bench/vendor/workspace/ml-federated-aggregation/anthropic_claude-opus-4.6_20260414_110948/flower/custom_fl_aggregation.py", line 497, in run_fl_simulation
updated_state, n_samples, avg_loss = client_local_train(
File "/srv/home/bohanlyu/MLS-Bench/vendor/workspace/ml-federated-aggregation/anthropic_claude-opus-4.6_20260414_110948/flower/custom_fl_aggregation.py", line 274, in client_l
AssistantStep 8submit
Tool Input
1{
2 "n": 2
3}Tool ResultStep 8
[submit] Submitting result from test #2 as final. ### cifar10 (scripts/cifar10.sh) Training metrics (last rounds): TRAIN_METRICS round=180 avg_loss=0.0919 round_time=4.9s TRAIN_METRICS round=185 avg_loss=0.0981 round_time=5.2s TRAIN_METRICS round=190 avg_loss=0.0359 round_time=4.4s TRAIN_METRICS round=195 avg_loss=0.0388 round_time=5.2s TRAIN_METRICS round=200 avg_loss=0.0662 round_time=4.5s Evaluation progress: EVAL round=160 test_loss=1.5424 test_accuracy=0.6323 elapsed=710s EVAL round=170 test_loss=1.6133 test_accuracy=0.6285 elapsed=765s EVAL round=180 test_loss=1.5337 test_accuracy=0.6385 elapsed=810s EVAL round=190 test_loss=1.3986 test_accuracy=0.6731 elapsed=859s EVAL round=200 test_loss=1.6660 test_accuracy=0.6411 elapsed=910s Final metrics (cifar10): test_accuracy_cifar10: 0.6411 test_loss_cifar10: 1.6660 best_accuracy_cifar10: 0.6731 ### femnist (scripts/femnist.sh) Training metrics (last rounds): TRAIN_METRICS round=180 avg_loss=0.0153 round_time=60.3s TRAIN_METRICS round