Proportional-Fair Resource Allocation and Dual-Threshold Early-Exit Inference for Secure Cooperative Multi-Layer Edge Intelligence
Paper recorded by Signals 4 on 2026-09-14 in cs.LG. Abstract reproduced from arXiv; link to the original below.
Published 2026-09-14 on arXiv · recorded by Signals 4 on 2026-09-15
Category: cs.LG · 机器学习 · first seen 2026-09-15
Abstract
This paper proposes FREDI (Fair Resource Allocation for Edge Dual-Threshold Inference), a secure wireless edge-intelligence framework for event-triggered inference in a cooperative user equipment (UE)--edge server (ES)--cloud system. Each UE performs early-exit convolutional neural network (CNN) screening using dual confidence thresholds, while critical events are securely offloaded to an edge ser
Read on arXiv →
Cite this page: Proportional-Fair Resource Allocation and Dual-Threshold Early-Exit Inference for Secure Cooperative Multi-Layer Edge Intelligence: the #55 most recent of 215 cs.LG papers we have recorded (as of 2026-09-14). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/proportional-fair-resource-allocation-and-dual-threshold-early-exit-inference-fo.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable:
papers.json
Get 4 AI signals a day by email — free.
Get 4 AI signals a day by email — free