Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Tailoring Semantic Communication at Network Edge: A Novel Approach Using Dynamic Knowledge Distillation

Abdullatif Albaseer, Mohamed Abdallah

TL;DR

This work addresses the challenge of delivering semantic communication at the network edge under heterogeneous compute and network conditions. It introduces a dynamic knowledge distillation framework that distills a server-side teacher model into lightweight edge SemEx models through a three-stage process, gradually increasing task complexity while honoring QoS constraints. The optimization is posed as a non-convex problem $P_1$ and reformulated to $P_2$ with a distillation budget $N_{ ext{Bdistilled}}$, and is solved via an iterative, pre-deployment approach that minimizes the KL divergence between server and edge representations. Experimental results on CIFAR-100 demonstrate that the proposed method achieves high semantic accuracy with significantly reduced edge computational load and lower communication energy, outperforming non-KD baselines and approaching static KD performance. This approach enables scalable, reliable SemCom for time-sensitive applications such as industrial automation and critical healthcare by aligning semantic extraction with device-specific capabilities and network conditions.

Abstract

Semantic Communication (SemCom) systems, empowered by deep learning (DL), represent a paradigm shift in data transmission. These systems prioritize the significance of content over sheer data volume. However, existing SemCom designs face challenges when applied to diverse computational capabilities and network conditions, particularly in time-sensitive applications. A key challenge is the assumption that diverse devices can uniformly benefit from a standard, large DL model in SemCom systems. This assumption becomes increasingly impractical, especially in high-speed, high-reliability applications such as industrial automation or critical healthcare. Therefore, this paper introduces a novel SemCom framework tailored for heterogeneous, resource-constrained edge devices and computation-intensive servers. Our approach employs dynamic knowledge distillation (KD) to customize semantic models for each device, balancing computational and communication constraints while ensuring Quality of Service (QoS). We formulate an optimization problem and develop an adaptive algorithm that iteratively refines semantic knowledge on edge devices, resulting in better models tailored to their resource profiles. This algorithm strategically adjusts the granularity of distilled knowledge, enabling devices to maintain high semantic accuracy for precise inference tasks, even under unstable network conditions. Extensive simulations demonstrate that our approach significantly reduces model complexity for edge devices, leading to better semantic extraction and achieving the desired QoS.

Tailoring Semantic Communication at Network Edge: A Novel Approach Using Dynamic Knowledge Distillation

TL;DR

This work addresses the challenge of delivering semantic communication at the network edge under heterogeneous compute and network conditions. It introduces a dynamic knowledge distillation framework that distills a server-side teacher model into lightweight edge SemEx models through a three-stage process, gradually increasing task complexity while honoring QoS constraints. The optimization is posed as a non-convex problem and reformulated to with a distillation budget , and is solved via an iterative, pre-deployment approach that minimizes the KL divergence between server and edge representations. Experimental results on CIFAR-100 demonstrate that the proposed method achieves high semantic accuracy with significantly reduced edge computational load and lower communication energy, outperforming non-KD baselines and approaching static KD performance. This approach enables scalable, reliable SemCom for time-sensitive applications such as industrial automation and critical healthcare by aligning semantic extraction with device-specific capabilities and network conditions.

Abstract

Semantic Communication (SemCom) systems, empowered by deep learning (DL), represent a paradigm shift in data transmission. These systems prioritize the significance of content over sheer data volume. However, existing SemCom designs face challenges when applied to diverse computational capabilities and network conditions, particularly in time-sensitive applications. A key challenge is the assumption that diverse devices can uniformly benefit from a standard, large DL model in SemCom systems. This assumption becomes increasingly impractical, especially in high-speed, high-reliability applications such as industrial automation or critical healthcare. Therefore, this paper introduces a novel SemCom framework tailored for heterogeneous, resource-constrained edge devices and computation-intensive servers. Our approach employs dynamic knowledge distillation (KD) to customize semantic models for each device, balancing computational and communication constraints while ensuring Quality of Service (QoS). We formulate an optimization problem and develop an adaptive algorithm that iteratively refines semantic knowledge on edge devices, resulting in better models tailored to their resource profiles. This algorithm strategically adjusts the granularity of distilled knowledge, enabling devices to maintain high semantic accuracy for precise inference tasks, even under unstable network conditions. Extensive simulations demonstrate that our approach significantly reduces model complexity for edge devices, leading to better semantic extraction and achieving the desired QoS.
Paper Structure (9 sections, 15 equations, 4 figures, 1 algorithm)

This paper contains 9 sections, 15 equations, 4 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model
  3. Problem Formulation
  4. Proposed Solution
  5. Stage 1: Establishing a Baseline with Simplified Task or Model
  6. Stage 2: Progressive Transition to Full Task Complexity
  7. Stage 3: Mastery of Original Task Complexity
  8. Performance Evaluation
  9. Conclusion

Figures (4)

  • Figure 1: The Task-Oriented SemCom System.
  • Figure 2: Inference accuracy of selected five devices for both the proposed approach and the baselines.
  • Figure 3: Computational Complexity for both the proposed approach and the baselines.
  • Figure 4: Normalized Transmit Power Consumption of selected five devices for both the proposed approach and the baselines.