Derives a high-probability PAC-Bayesian bound on wireless generalization error for edge inference and proposes a channel-aware training algorithm minimizing a surrogate of the bound.
Split learning over wireless networks: Parallel design and resource management,
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
Collaborative LLM inference on LEO satellite networks via model splitting, pipeline parallelism, and adaptive compression reduces inference delay by up to 42% and communication overhead by up to 71% with less than 1% accuracy loss.
citing papers explorer
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A PAC-Bayesian Analysis of Channel-Induced Degradation in Edge Inference
Derives a high-probability PAC-Bayesian bound on wireless generalization error for edge inference and proposes a channel-aware training algorithm minimizing a surrogate of the bound.
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Communication-Efficient Collaborative LLM Inference over LEO Satellite Networks
Collaborative LLM inference on LEO satellite networks via model splitting, pipeline parallelism, and adaptive compression reduces inference delay by up to 42% and communication overhead by up to 71% with less than 1% accuracy loss.