Distillation-Enabled Knowledge Alignment for Generative Semantic Communications in AIGC Provisioning Tasks

• URL: http://arxiv.org/abs/2506.19893v1
• Date: Tue, 24 Jun 2025 10:50:14 GMT
• Title: Distillation-Enabled Knowledge Alignment for Generative Semantic Communications in AIGC Provisioning Tasks
• Authors: Jingzhi Hu, Geoffrey Ye Li,
• Abstract summary: Generative semantic communication (GSC) offers a promising solution by transmitting highly compact information.<n>GSC relies on the alignment between the knowledge in the cloud generative AI (GAI) and that possessed by the edges and users.<n>We propose DeKA-g, a distillation-enabled knowledge alignment algorithm for GSC systems.
• Score: 38.5438416972178
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Due to the surging amount of AI-generated content (AIGC), its provisioning to edges and mobile users from the cloud incurs substantial traffic on networks. Generative semantic communication (GSC) offers a promising solution by transmitting highly compact information, i.e., prompt text and latent representations, instead of high-dimensional AIGC data. However, GSC relies on the alignment between the knowledge in the cloud generative AI (GAI) and that possessed by the edges and users, and between the knowledge for wireless transmission and that of actual channels, which remains challenging. In this paper, we propose DeKA-g, a distillation-enabled knowledge alignment algorithm for GSC systems. The core idea is to distill the generation knowledge from the cloud-GAI into low-rank matrices, which can be incorporated by the edge and used to adapt the transmission knowledge to diverse wireless channel conditions. DeKA-g comprises two novel methods: metaword-aided knowledge distillation (MAKD) and variable-rate grouped SNR adaptation (VGSA). For MAKD, an optimized metaword is employed to enhance the efficiency of knowledge distillation, while VGSA enables efficient adaptation to diverse compression rates and SNR ranges. From simulation results, DeKA-g improves the alignment between the edge-generated images and the cloud-generated ones by 44%. Moreover, it adapts to compression rates with 116% higher efficiency than the baseline and enhances the performance in low-SNR conditions by 28%.

Related papers

• AI-Powered Hybrid Intrusion Detection Framework for Cloud Security Using Novel Metaheuristic Optimization [1.3318026799252651]
  This study presents the Hybrid Intrusion Detection System (HyIDS) that employs the Energy Valley (EVO) for Feature Selection (FS)<n>Twenty-four trials were done, revealing substantial enhancements in categorization accuracy, precision, and recall.<n>These data demonstrate that EVO significantly improves cybersecurity in Cloud Computing (CC)
  arXiv  Detail & Related papers  (2026-01-03T09:42:28Z)
• Security-Aware Joint Sensing, Communication, and Computing Optimization in Low Altitude Wireless Networks [83.84711311344918]
  Integrated sensing, communications, and computing (I SCC) is one of the core parts of Low-Altitude Wireless Networks (LAWNs)<n>This paper studies joint performance optimization of I SCC while considering secrecyness of the communications.<n>We propose a deep Q-network (DQN)-based multi-objective evolutionary algorithm, which adaptively selects evolutionary operators according to the evolving optimization objectives.
  arXiv  Detail & Related papers  (2025-11-03T11:06:41Z)
• Securing IoT Communications via Anomaly Traffic Detection: Synergy of Genetic Algorithm and Ensemble Method [0.0]
  The rapid growth of the Internet of Things has transformed industries by enabling seamless data exchange among connected devices.<n> IoT networks remain vulnerable to security threats such as denial of service (DoS) attacks, anomalous traffic, and data manipulation.<n>This paper proposes an advanced anomaly detection framework with three main phases.
  arXiv  Detail & Related papers  (2025-10-21T22:42:05Z)
• Channel Fingerprint Construction for Massive MIMO: A Deep Conditional Generative Approach [65.47969413708344]
  We introduce the concept of CF twins and design a conditional generative diffusion model (CGDM)<n>We employ a variational inference technique to derive the evidence lower bound (ELBO) for the log-marginal distribution of the observed fine-grained CF conditioned on the coarse-grained CF.<n>We show that the proposed approach exhibits significant improvement in reconstruction performance compared to the baselines.
  arXiv  Detail & Related papers  (2025-05-12T01:36:06Z)
• Knowledge Distillation: Enhancing Neural Network Compression with Integrated Gradients [0.0]
  This paper proposes a machine learning framework that augments Knowledge Distillation (KD) with Integrated Gradients (IG)<n>We introduce a novel data augmentation strategy where IG maps, precomputed from a teacher model, are overlaid onto training images to guide a compact student model toward critical feature representations.<n>Experiments on CIFAR-10 demonstrate the efficacy of our method: a student model, compressed 4.1-fold from the MobileNet-V2 teacher, achieves 92.5% classification accuracy, surpassing the baseline student's 91.4% and traditional KD approaches, while reducing inference latency from 140 ms to 13 ms--a tenfold
  arXiv  Detail & Related papers  (2025-03-17T10:07:50Z)
• PCE-GAN: A Generative Adversarial Network for Point Cloud Attribute Quality Enhancement based on Optimal Transport [56.56430888985025]
  We propose a generative adversarial network for point cloud quality enhancement (PCE-GAN)<n>The generator consists of a local feature extraction (LFE) unit, a global spatial correlation (GSC) unit and a feature squeeze unit.<n>The discriminator computes the deviation between the probability distributions of the enhanced point cloud and the original point cloud, guiding the generator to achieve high quality reconstruction.
  arXiv  Detail & Related papers  (2025-02-26T07:34:33Z)
• AI-in-the-Loop Sensing and Communication Joint Design for Edge Intelligence [65.29835430845893]
  We propose a framework that enhances edge intelligence through AI-in-the-loop joint sensing and communication.<n>A key contribution of our work is establishing an explicit relationship between validation loss and the system's tunable parameters.<n>We show that our framework reduces communication energy consumption by up to 77 percent and sensing costs measured by the number of samples by up to 52 percent.
  arXiv  Detail & Related papers  (2025-02-14T14:56:58Z)
• A Wireless AI-Generated Content (AIGC) Provisioning Framework Empowered by Semantic Communication [53.78269720999609]
  This paper proposes a semantic communication (SemCom)-empowered AIGC (SemAIGC) generation and transmission framework. Specifically, SemAIGC integrates diffusion models within the semantic encoder and decoder to design a workload-adjustable transceiver. Simulations verify the superiority of our proposed SemAIGC framework in terms of latency and content quality compared to conventional approaches.
  arXiv  Detail & Related papers  (2023-10-26T18:05:22Z)
• Enabling AI-Generated Content (AIGC) Services in Wireless Edge Networks [68.00382171900975]
  In wireless edge networks, the transmission of incorrectly generated content may unnecessarily consume network resources. We present the AIGC-as-a-service concept and discuss the challenges in deploying A at the edge networks. We propose a deep reinforcement learning-enabled algorithm for optimal ASP selection.
  arXiv  Detail & Related papers  (2023-01-09T09:30:23Z)
• Effective classification of ecg signals using enhanced convolutional neural network in iot [0.0]
  This paper proposes a routing system for IoT healthcare platforms based on Dynamic Source Routing (DSR) and Routing by Energy and Link Quality (REL) Deep-ECG will employ a deep CNN to extract important characteristics, which will then be compared using simple and fast distance functions. The results show that the proposed strategy outperforms others in terms of classification accuracy.
  arXiv  Detail & Related papers  (2022-02-08T13:37:23Z)
• Conditionally Deep Hybrid Neural Networks Across Edge and Cloud [6.442928757682793]
  We propose a conditionally deep hybrid neural network for enabling AI-based fog computing. The proposed network can be deployed in a distributed manner, consisting of quantized layers and early exits at the edge and full-precision layers on the cloud. We show that with binarized layers at the edge, the proposed conditional hybrid network can process 65% of inferences at the edge, leading to 5.5x computational energy reduction.
  arXiv  Detail & Related papers  (2020-05-21T18:18:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.