Research map / Federated learning and privacy / Communication-efficient FL
Federated Adversarial Learning: A Framework with Convergence Analysis
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Adding PGD-based adversarial training on clients (FAL) vs. standard FedAvg, compared on accuracy curves of synthetic binary classification | federated adversarial learning on synthetic data · Synthetic data (2D Gaussian clusters, 4 clients) | accuracy convergence round | No clear effect |
| Higher data separability (larger gamma) in synthetic data (ablation) | federated adversarial learning step size · Synthetic data | convergence speed and admissible learning rate | Helped |
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