FSSC: Federated Learning of Transformer Neural Networks for Semantic Image Communication

• URL: http://arxiv.org/abs/2407.21507v1
• Date: Wed, 31 Jul 2024 10:25:24 GMT
• Title: FSSC: Federated Learning of Transformer Neural Networks for Semantic Image Communication
• Authors: Yuna Yan, Xin Zhang, Lixin Li, Wensheng Lin, Rui Li, Wenchi Cheng, Zhu Han,
• Abstract summary: We address the problem of image semantic communication in a multi-user deployment scenario. We propose a federated learning (FL) strategy for a Swin Transformer-based semantic communication system.
• Score: 27.79514340995533
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we address the problem of image semantic communication in a multi-user deployment scenario and propose a federated learning (FL) strategy for a Swin Transformer-based semantic communication system (FSSC). Firstly, we demonstrate that the adoption of a Swin Transformer for joint source-channel coding (JSCC) effectively extracts semantic information in the communication system. Next, the FL framework is introduced to collaboratively learn a global model by aggregating local model parameters, rather than directly sharing clients' data. This approach enhances user privacy protection and reduces the workload on the server or mobile edge. Simulation evaluations indicate that our method outperforms the typical JSCC algorithm and traditional separate-based communication algorithms. Particularly after integrating local semantics, the global aggregation model has further increased the Peak Signal-to-Noise Ratio (PSNR) by more than 2dB, thoroughly proving the effectiveness of our algorithm.

Related papers

• Modality Alignment Meets Federated Broadcasting [9.752555511824593]
  Federated learning (FL) has emerged as a powerful approach to safeguard data privacy by training models across distributed edge devices without centralizing local data. This paper introduces a novel FL framework leveraging modality alignment, where a text encoder resides on the server, and image encoders operate on local devices.
  arXiv  Detail & Related papers  (2024-11-24T13:30:03Z)
• Personalized Federated Learning for Cross-view Geo-localization [49.40531019551957]
  We propose a methodology combining Federated Learning (FL) with Cross-view Image Geo-localization (CVGL) techniques. Our method implements a coarse-to-fine approach, where clients share only the coarse feature extractors while keeping fine-grained features specific to local environments. Results demonstrate that our federated CVGL method achieves performance close to centralized training while maintaining data privacy.
  arXiv  Detail & Related papers  (2024-11-07T13:25:52Z)
• Personalized Federated Learning for Generative AI-Assisted Semantic Communications [29.931169585178818]
  We propose a GAI-assisted Semantic Communication (SC) model deployed between Mobile Users (MUs) and the Base Station (BS) To train the GSC model using the local data of MUs, we introduce Personalized Semantic Federated Learning (PSFL) In PLD, each MU selects a personalized GSC model as a mentor tailored to its local resources and a unified Convolutional Neural Networks (CNN)-based SC (CSC) model as a student. In AGP, we perform network pruning on the aggregated global model according to real-time communication environments, reducing communication energy.
  arXiv  Detail & Related papers  (2024-10-03T12:52:36Z)
• CNN-Transformer Rectified Collaborative Learning for Medical Image Segmentation [60.08541107831459]
  This paper proposes a CNN-Transformer rectified collaborative learning framework to learn stronger CNN-based and Transformer-based models for medical image segmentation. Specifically, we propose a rectified logit-wise collaborative learning (RLCL) strategy which introduces the ground truth to adaptively select and rectify the wrong regions in student soft labels. We also propose a class-aware feature-wise collaborative learning (CFCL) strategy to achieve effective knowledge transfer between CNN-based and Transformer-based models in the feature space.
  arXiv  Detail & Related papers  (2024-08-25T01:27:35Z)
• Semantic Successive Refinement: A Generative AI-aided Semantic Communication Framework [27.524671767937512]
  We introduce a novel Generative AI Semantic Communication (GSC) system for single-user scenarios. At the transmitter end, it employs a joint source-channel coding mechanism based on the Swin Transformer for efficient semantic feature extraction. At the receiver end, an advanced Diffusion Model (DM) reconstructs high-quality images from degraded signals, enhancing perceptual details.
  arXiv  Detail & Related papers  (2024-07-31T06:08:51Z)
• Efficient Model Compression for Hierarchical Federated Learning [10.37403547348343]
  Federated learning (FL) has garnered significant attention due to its capacity to preserve privacy within distributed learning systems. This paper introduces a novel hierarchical FL framework that integrates the benefits of clustered FL and model compression.
  arXiv  Detail & Related papers  (2024-05-27T12:17:47Z)
• Causal Semantic Communication for Digital Twins: A Generalizable Imitation Learning Approach [74.25870052841226]
  A digital twin (DT) leverages a virtual representation of the physical world, along with communication (e.g., 6G), computing, and artificial intelligence (AI) technologies to enable many connected intelligence services. Wireless systems can exploit the paradigm of semantic communication (SC) for facilitating informed decision-making under strict communication constraints. A novel framework called causal semantic communication (CSC) is proposed for DT-based wireless systems.
  arXiv  Detail & Related papers  (2023-04-25T00:15:00Z)
• Optimization of Image Transmission in a Cooperative Semantic Communication Networks [68.2233384648671]
  A semantic communication framework for image transmission is developed. Servers cooperatively transmit images to a set of users utilizing semantic communication techniques. A multimodal metric is proposed to measure the correlation between the extracted semantic information and the original image.
  arXiv  Detail & Related papers  (2023-01-01T15:59:13Z)
• Perceptual Learned Source-Channel Coding for High-Fidelity Image Semantic Transmission [7.692038874196345]
  In this paper, we introduce adversarial losses to optimize deep J SCC. Our new deep J SCC architecture combines encoder, wireless channel, decoder/generator, and discriminator. A user study confirms that achieving the perceptually similar end-to-end image transmission quality, the proposed method can save about 50% wireless channel bandwidth cost.
  arXiv  Detail & Related papers  (2022-05-26T03:05:13Z)
• Federated Stochastic Gradient Descent Begets Self-Induced Momentum [151.4322255230084]
  Federated learning (FL) is an emerging machine learning method that can be applied in mobile edge systems. We show that running to the gradient descent (SGD) in such a setting can be viewed as adding a momentum-like term to the global aggregation process.
  arXiv  Detail & Related papers  (2022-02-17T02:01:37Z)
• Communication-Efficient and Distributed Learning Over Wireless Networks: Principles and Applications [55.65768284748698]
  Machine learning (ML) is a promising enabler for the fifth generation (5G) communication systems and beyond. This article aims to provide a holistic overview of relevant communication and ML principles, and thereby present communication-efficient and distributed learning frameworks with selected use cases.
  arXiv  Detail & Related papers  (2020-08-06T12:37:14Z)

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.