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#federated learning Open access

Precision and Coverage in Robust Federated Learning: Code and Reproducibility Evidence

Sep 2026 · Figshare

Abstract

This deposit provides the software and reproducibility evidence for Precision and Coverage in Robust Federated Learning. It includes compressed client-update codecs, robust aggregation, seven development candidates and a complete five-seed comparison of three methods. Archived checkpoints, predictions, confusion matrices, traffic logs and readable result tables support reviewer inspection. The selected method achieves 73.654% mean final test accuracy, with a sample standard deviation of 0.383 percentage points, under the specified CIFAR-10 simulation. Reviewers can verify saved evidence without retraining. Full training instructions are also provided. The evaluation covers one dataset and one specified attack configuration. After extraction, reviewers can run:python -m pip install -r requirements-review.txtpython review.py --output ../review_verification.json

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