Research map / Learning theory and algorithms / Sample complexity
Collaborative Learning in the Jungle (Decentralized, Byzantine, Heterogeneous, Asynchronous and Nonconvex Learning)
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use i.i.d.-data variant instead of non-i.i.d. variant of the agreement algorithms (ablation) | MDA and RB-TM collaborative learning · throughput overhead, 10 nodes | throughput | Helped |
| use MDA-based algorithm instead of RB-TM (reliable broadcast + trimmed mean) | Byzantine collaborative learning · throughput overhead, 10 nodes | throughput | Helped |
| use robust algorithms instead of vanilla averaging | ResNet-18 on CIFAR-10, 7 nodes · CIFAR-10 convergence per epoch | convergence | No clear effect |
| small models instead of large models under robust algorithms | MDA and RB-TM · throughput overhead | slowdown | Hurt |
Closest papers by meaning
- Byzantine-Robust Decentralized Learning via Remove-then-Clip Aggregation (AAAI 2024 · similarity 0.87)
- Byzantine Robustness and Partial Participation Can Be Achieved at Once: Just Clip Gradient Differences (NeurIPS 2024 · similarity 0.86)
- From Inexact Gradients to Byzantine Robustness: Acceleration and Optimization under Similarity (NeurIPS 2026 · similarity 0.86)
- Weight for Robustness: A Comprehensive Approach towards Optimal Fault-Tolerant Asynchronous ML (NeurIPS 2024 · similarity 0.86)
- Fault Tolerant ML: Efficient Meta-Aggregation and Synchronous Training (ICML 2024 · similarity 0.85)
- On the Tension between Byzantine Robustness and No-Attack Accuracy in Distributed Learning (ICML 2025 · similarity 0.85)
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