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Denial of Deadline: Network-Driven Accuracy Collapse in Distributed Inference Pipelines
arXiv cs.AI · 发布于 · 阅读约3分钟
30秒读懂
- 发生了什么
- Researchers demonstrate that network congestion attacks can degrade accuracy in distributed inference systems that combine fast local and slow remote predictions.
- 为何重要
- Engineers building edge-cloud inference pipelines for autonomous driving, real-time tracking, or latency-sensitive applications should understand this vulnerability.
- 注意
- The attack requires no model access or victim data, only ability to generate shaped traffic bursts that delay remote predictions past application deadlines.
收听本摘要
- inference
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