When Scaling Fails: Network and Fabric Effects on Distributed GPU Training Performance
2026-03-06T07:23:53Z•f091a4f6a685728855241da77b0dad1d6ede3c98daa82eb44fc16c878ee3085e
DRL auction manipulationEnvoy external processorLLM routingO-CUO-DUO-RANO-RUPII leakageRI S allocationadversarial MLautomotive/avionics safetycellular securitycongestion dynamicsdenial-of-servicedistributed GPU traininginference privacyjailbreak detectionlegacy 2G/3Gmessage groupingmulti-cloud routingnetwork fabricnetwork topologysynchronization amplificationtime-triggered schedulingvLLM Semantic Router
What happened
This collection of arXiv papers surveys systems research across distributed GPU training, cellular/O-RAN networks, connected-vehicle traffic prediction, RIS allocation with DRL, deterministic time-triggered scheduling, green mesh wireless architectures, and multi-model routing for LLMs (vLLM Semantic Router). Common themes with security relevance include expanded attack surfaces from disaggregation and multi-provider deployments (O-RAN, vLLM multi-cloud routing, Envoy external processors), legacy/under‑maintained infrastructure (2G/3G towers), and subtle network/fabric effects (topology, Conga
Why it matters
A reviewed impact interpretation has not been published for this record.
Evidence and limitations
- Source ID
- arxiv_cs_ni
- Record identifier
- f091a4f6a685728855241da77b0dad1d6ede3c98daa82eb44fc16c878ee3085e
- Enrichment time
- 2026-03-06T07:23:53Z
- AI-assisted enrichment
- Yes
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