Proportional-Fair Resource Allocation and Dual-Threshold Early-Exit Inference for Secure Cooperative Multi-Layer Edge Intelligence

arXiv:2609.15847 · cs.NI, cs.CV, cs.ET, cs.LG · Submitted 2026-09-14 · Read on arXiv

cs.NI, cs.CV, cs.ET, cs.LG

Submitted: 2026-09-14

Updated: 2026-09-14

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

Trace — 6× in this paper · explained in 2 episodes
Benchmark — 5× in this paper · explained in 2 episodes
Edge AI — 3× in this paper · explained in 2 episodes
Fair Resource Allocation for Edge Dual-Threshold Inference — 3× in this paper
Consistency — 2× in this paper · explained in 3 episodes
Decoupling — 2× in this paper · explained in 2 episodes
EMMA — 2× in this paper · explained in 2 episodes
Pruning — 2× in this paper · explained in 4 episodes
Ablation — 1× in this paper · explained in 1 episode
Accuracy — 1× in this paper · explained in 1 episode
Aggregation — 1× in this paper · explained in 1 episode
Convolutional Neural Networks (CNNs) — 1× in this paper · explained in 2 episodes
Digital Twin — 1× in this paper · explained in 5 episodes
F1 Score — 1× in this paper · explained in 3 episodes
Federated Learning — 1× in this paper · explained in 15 episodes
Hybrid Models — 1× in this paper · explained in 2 episodes
Mobile Edge Computing — 1× in this paper · explained in 2 episodes
Reinforcement Learning — 1× in this paper · explained in 4 episodes