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Distributed Newton Can Communicate Less and Resist Byzantine Workers
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Send the local inverse-Hessian–gradient product in one communication round instead of sequential gradient and Hessian-related messages | COMRADE · LIBSVM classification datasets | training accuracy | No clear effect |
| Use norm-based thresholding with COMRADE's second-order updates rather than GIANT's two-round procedure with thresholding on local gradient norms | a9a · a9a, 10% Byzantine workers | training accuracy | Helped |
| Add norm-based thresholding to GIANT | GIANT · a9a, flipped-label and negative-update attacks | training accuracy | Mixed |
| Add norm-based thresholding that trims the largest-norm updates | COMRADE · LIBSVM classification datasets under Byzantine attacks | classification accuracy | Helped |
| Compress worker updates with a QSGD-based approximate compressor (ablation) | COMRADE · w5a and a9a under Byzantine attacks | training accuracy | No clear effect |
| Add a ρ-approximate compressor to worker updates (ablation) | COMRADE · Strongly convex optimization | convergence rate | No clear effect |
Closest papers by meaning
- Communication Efficient Distributed Newton Method over Unreliable Networks (AAAI 2024 · similarity 0.88)
- Byzantine Robustness and Partial Participation Can Be Achieved at Once: Just Clip Gradient Differences (NeurIPS 2024 · similarity 0.88)
- From Inexact Gradients to Byzantine Robustness: Acceleration and Optimization under Similarity (NeurIPS 2026 · similarity 0.87)
- On the Tension between Byzantine Robustness and No-Attack Accuracy in Distributed Learning (ICML 2025 · similarity 0.86)
- Byzantine-Robust Decentralized Learning via Remove-then-Clip Aggregation (AAAI 2024 · similarity 0.86)
- Resilient Byzantine Agreement with Predictions (NeurIPS 2026 · similarity 0.85)
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