An Efficient Privacy-aware Split Learning Framework for Satellite Communications

• URL: http://arxiv.org/abs/2409.08538v2
• Date: Tue, 12 Nov 2024 04:19:03 GMT
• Title: An Efficient Privacy-aware Split Learning Framework for Satellite Communications
• Authors: Jianfei Sun, Cong Wu, Shahid Mumtaz, Junyi Tao, Mingsheng Cao, Mei Wang, Valerio Frascolla,
• Abstract summary: We propose a novel framework for more efficient SL in satellite communications. Our approach, Dynamic Topology Informed Pruning, combines differential privacy with graph and model pruning to optimize graph neural networks for distributed learning. Our framework not only significantly improves the operational efficiency of satellite communications but also establishes a new benchmark in privacy-aware distributed learning.
• Score: 33.608696987158424
• License:
• Abstract: In the rapidly evolving domain of satellite communications, integrating advanced machine learning techniques, particularly split learning, is crucial for enhancing data processing and model training efficiency across satellites, space stations, and ground stations. Traditional ML approaches often face significant challenges within satellite networks due to constraints such as limited bandwidth and computational resources. To address this gap, we propose a novel framework for more efficient SL in satellite communications. Our approach, Dynamic Topology Informed Pruning, namely DTIP, combines differential privacy with graph and model pruning to optimize graph neural networks for distributed learning. DTIP strategically applies differential privacy to raw graph data and prunes GNNs, thereby optimizing both model size and communication load across network tiers. Extensive experiments across diverse datasets demonstrate DTIP's efficacy in enhancing privacy, accuracy, and computational efficiency. Specifically, on Amazon2M dataset, DTIP maintains an accuracy of 0.82 while achieving a 50% reduction in floating-point operations per second. Similarly, on ArXiv dataset, DTIP achieves an accuracy of 0.85 under comparable conditions. Our framework not only significantly improves the operational efficiency of satellite communications but also establishes a new benchmark in privacy-aware distributed learning, potentially revolutionizing data handling in space-based networks.

Related papers

• The Robustness of Spiking Neural Networks in Communication and its Application towards Network Efficiency in Federated Learning [6.9569682335746235]
  Spiking Neural Networks (SNNs) have recently gained significant interest in on-chip learning in embedded devices. In this paper, we explore the inherent robustness of SNNs under noisy communication in Federated Learning. We propose a novel Federated Learning with TopK Sparsification algorithm to reduce the bandwidth usage for FL training.
  arXiv  Detail & Related papers  (2024-09-19T13:37:18Z)
• Hyperdimensional Computing Empowered Federated Foundation Model over Wireless Networks for Metaverse [56.384390765357004]
  We propose an integrated federated split learning and hyperdimensional computing framework for emerging foundation models. This novel approach reduces communication costs, computation load, and privacy risks, making it suitable for resource-constrained edge devices in the Metaverse.
  arXiv  Detail & Related papers  (2024-08-26T17:03:14Z)
• Cross-domain Learning Framework for Tracking Users in RIS-aided Multi-band ISAC Systems with Sparse Labeled Data [55.70071704247794]
  Integrated sensing and communications (ISAC) is pivotal for 6G communications and is boosted by the rapid development of reconfigurable intelligent surfaces (RISs) This paper proposes the X2Track framework, where we model the tracking function by a hierarchical architecture, jointly utilizing multi-modal CSI indicators across multiple bands, and optimize it in a cross-domain manner. Under X2Track, we design an efficient deep learning algorithm to minimize tracking errors, based on transformer neural networks and adversarial learning techniques.
  arXiv  Detail & Related papers  (2024-05-10T08:04:27Z)
• Energy-Efficient On-Board Radio Resource Management for Satellite Communications via Neuromorphic Computing [59.40731173370976]
  We investigate the application of energy-efficient brain-inspired machine learning models for on-board radio resource management. For relevant workloads, spiking neural networks (SNNs) implemented on Loihi 2 yield higher accuracy, while reducing power consumption by more than 100$times$ as compared to the CNN-based reference platform.
  arXiv  Detail & Related papers  (2023-08-22T03:13:57Z)
• Collaborative Learning with a Drone Orchestrator [79.75113006257872]
  A swarm of intelligent wireless devices train a shared neural network model with the help of a drone. The proposed framework achieves a significant speedup in training, leading to an average 24% and 87% saving in the drone hovering time.
  arXiv  Detail & Related papers  (2023-03-03T23:46:25Z)
• Olive Branch Learning: A Topology-Aware Federated Learning Framework for Space-Air-Ground Integrated Network [19.059950250921926]
  Training AI models centrally with the assistance of SAGIN faces the challenges of highly constrained network topology, inefficient data transmission, and privacy issues. We first propose a novel topology-aware federated learning framework for the SAGIN, namely Olive Branch Learning (OBL) We extend our OBL framework and CNASA algorithm to adapt to more complex multi-orbit satellite networks.
  arXiv  Detail & Related papers  (2022-12-02T14:51:42Z)
• Transfer Learning Based Efficient Traffic Prediction with Limited Training Data [3.689539481706835]
  Efficient prediction of internet traffic is an essential part of Self Organizing Network (SON) for ensuring proactive management. Deep sequence model in network traffic prediction with limited training data has not been studied extensively in the current works. We investigated and evaluated the performance of the deep transfer learning technique in traffic prediction with inadequate historical data.
  arXiv  Detail & Related papers  (2022-05-09T14:44:39Z)
• FedDUAP: Federated Learning with Dynamic Update and Adaptive Pruning Using Shared Data on the Server [64.94942635929284]
  Federated Learning (FL) suffers from two critical challenges, i.e., limited computational resources and low training efficiency. We propose a novel FL framework, FedDUAP, to exploit the insensitive data on the server and the decentralized data in edge devices. By integrating the two original techniques together, our proposed FL model, FedDUAP, significantly outperforms baseline approaches in terms of accuracy (up to 4.8% higher), efficiency (up to 2.8 times faster), and computational cost (up to 61.9% smaller)
  arXiv  Detail & Related papers  (2022-04-25T10:00:00Z)
• Semi-Decentralized Federated Edge Learning with Data and Device Heterogeneity [6.341508488542275]
  Federated edge learning (FEEL) has attracted much attention as a privacy-preserving paradigm to effectively incorporate the distributed data at the network edge for training deep learning models. In this paper, we investigate a novel framework of FEEL, namely semi-decentralized federated edge learning (SD-FEEL), where multiple edge servers are employed to collectively coordinate a large number of client nodes. By exploiting the low-latency communication among edge servers for efficient model sharing, SD-FEEL can incorporate more training data, while enjoying much lower latency compared with conventional federated learning.
  arXiv  Detail & Related papers  (2021-12-20T03:06:08Z)
• Learning to Continuously Optimize Wireless Resource in a Dynamic Environment: A Bilevel Optimization Perspective [52.497514255040514]
  This work develops a new approach that enables data-driven methods to continuously learn and optimize resource allocation strategies in a dynamic environment. We propose to build the notion of continual learning into wireless system design, so that the learning model can incrementally adapt to the new episodes. Our design is based on a novel bilevel optimization formulation which ensures certain fairness" across different data samples.
  arXiv  Detail & Related papers  (2021-05-03T07:23:39Z)

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.