HintKD: Hint Knowledge Distillation for Bandwidth-Constrained Cloud-Edge Inference

2026-10-06

AI Summary

This study addresses the excessive communication overhead caused by transmitting high-dimensional features or soft labels in cloud-edge collaborative inference. To mitigate this, we propose HintKD, a framework that replaces high-dimensional data transmission with discrete hints of O(1) complexity, where the cloud server sends only indices. By integrating maximum conditional mutual information training, differentiable vector quantization, and lightweight FiLM adapters, HintKD adaptively modulates student features at the edge to facilitate knowledge distillation. This design substantially reduces communication load while effectively preserving intra-class variation information. Experimental evaluations on the DeepSense 6G and MNIST datasets demonstrate that HintKD achieves a superior accuracy-bandwidth trade-off compared to existing approaches.

Abstract

Collaborative inference between the edge cloud and user equipment (UE) is a promising paradigm for deploying large deep neural networks (DNNs) in 6G networks. However, existing cloud-edge inference and distillation schemes often require the real-time transmission of high-dimensional intermediate features or soft probability vectors, which imposes a substantial communication burden on bandwidth-limited wireless links. To address this challenge, we propose HintKD, a bandwidth-constrained distillation framework for cloud-edge inference. The core idea is to compress the teacher's guidance into compact discrete hints rather than transmit raw features directly. Specifically, a cloud-side teacher is first trained with a maximum conditional mutual information (MCMI) objective to preserve informative intra-class variations. Its latent representation is then mapped to a compact codebook through differentiable vector quantization. During deployment, the cloud transmits only a hint index, and the UE uses a lightweight FiLM-based adapter to modulate student features according to the received codeword. Experiments on the DeepSense 6G beam prediction task show that HintKD preserves competitive accuracy while reducing the inference-side communication payload from O(D_t) for feature transmission or O(C) for logit transmission to O(1) for discrete hint transmission. In addition, experiments on the MNIST dataset further verify the generality of the proposed distillation method across different datasets and tasks. These results show that HintKD achieves a favorable accuracy-bandwidth trade-off by replacing high-dimensional feature or logit transmission with a compact discrete hint index.

Problem

• cloud-edge inference
• knowledge distillation
• bandwidth-constrained
• communication overhead
• 6G networks

Innovation

• Knowledge Distillation
• Cloud-Edge Inference
• Vector Quantization
• Bandwidth-Constrained
• FiLM Adapter