Satellite Federated Edge Learning: Architecture Design and Convergence Analysis

• URL: http://arxiv.org/abs/2404.01875v1
• Date: Tue, 2 Apr 2024 11:59:58 GMT
• Title: Satellite Federated Edge Learning: Architecture Design and Convergence Analysis
• Authors: Yuanming Shi, Li Zeng, Jingyang Zhu, Yong Zhou, Chunxiao Jiang, Khaled B. Letaief,
• Abstract summary: This paper introduces a novel FEEL algorithm, named FEDMEGA, tailored to mega-constellation networks. By integrating inter-satellite links (ISL) for intra-orbit model aggregation, the proposed algorithm significantly reduces the usage of low data rate and intermittent GSL. Our proposed method includes a ring all-reduce based intra-orbit aggregation mechanism, coupled with a network flow-based transmission scheme for global model aggregation.
• Score: 47.057886812985984
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The proliferation of low-earth-orbit (LEO) satellite networks leads to the generation of vast volumes of remote sensing data which is traditionally transferred to the ground server for centralized processing, raising privacy and bandwidth concerns. Federated edge learning (FEEL), as a distributed machine learning approach, has the potential to address these challenges by sharing only model parameters instead of raw data. Although promising, the dynamics of LEO networks, characterized by the high mobility of satellites and short ground-to-satellite link (GSL) duration, pose unique challenges for FEEL. Notably, frequent model transmission between the satellites and ground incurs prolonged waiting time and large transmission latency. This paper introduces a novel FEEL algorithm, named FEDMEGA, tailored to LEO mega-constellation networks. By integrating inter-satellite links (ISL) for intra-orbit model aggregation, the proposed algorithm significantly reduces the usage of low data rate and intermittent GSL. Our proposed method includes a ring all-reduce based intra-orbit aggregation mechanism, coupled with a network flow-based transmission scheme for global model aggregation, which enhances transmission efficiency. Theoretical convergence analysis is provided to characterize the algorithm performance. Extensive simulations show that our FEDMEGA algorithm outperforms existing satellite FEEL algorithms, exhibiting an approximate 30% improvement in convergence rate.

Related papers

• Communication-Efficient Federated Learning by Quantized Variance Reduction for Heterogeneous Wireless Edge Networks [55.467288506826755]
  Federated learning (FL) has been recognized as a viable solution for local-privacy-aware collaborative model training in wireless edge networks. Most existing communication-efficient FL algorithms fail to reduce the significant inter-device variance. We propose a novel communication-efficient FL algorithm, named FedQVR, which relies on a sophisticated variance-reduced scheme.
  arXiv  Detail & Related papers  (2025-01-20T04:26:21Z)
• Improving Traffic Flow Predictions with SGCN-LSTM: A Hybrid Model for Spatial and Temporal Dependencies [55.2480439325792]
  This paper introduces the Signal-Enhanced Graph Convolutional Network Long Short Term Memory (SGCN-LSTM) model for predicting traffic speeds across road networks. Experiments on the PEMS-BAY road network traffic dataset demonstrate the SGCN-LSTM model's effectiveness.
  arXiv  Detail & Related papers  (2024-11-01T00:37:00Z)
• SatFed: A Resource-Efficient LEO Satellite-Assisted Heterogeneous Federated Learning Framework [19.59862482196897]
  We propose SatFed, a resource-efficient satellite-assisted heterogeneous FL framework. SatFed implements freshness-based model prioritization queues to optimize the use of highly constrained satellite-ground bandwidth. Experiments with real-world LEO satellite networks demonstrate that SatFed achieves superior performance and robustness compared to state-of-the-art benchmarks.
  arXiv  Detail & Related papers  (2024-09-20T13:44:00Z)
• A Distance Similarity-based Genetic Optimization Algorithm for Satellite Ground Network Planning Considering Feeding Mode [53.71516191515285]
  The low transmission efficiency of the satellite data relay back mission has become a problem that is currently constraining the construction of the system. We propose a distance similarity-based genetic optimization algorithm (DSGA), which considers the state characteristics between the tasks and introduces a weighted Euclidean distance method to determine the similarity between the tasks.
  arXiv  Detail & Related papers  (2024-08-29T06:57:45Z)
• Hierarchical Learning and Computing over Space-Ground Integrated Networks [40.19542938629252]
  We propose a hierarchical learning and computing framework to provide global aggregation services for locally trained models on ground IoT devices. We formulate a network energy problem for model aggregation, which turns out to be a Directed Steiner Tree (DST) problem. We propose a topologyaware energy-efficient routing (TAEER) algorithm to solve the DST problem by finding a minimum spanning arborescence on a substitute directed graph.
  arXiv  Detail & Related papers  (2024-08-26T09:05:43Z)
• Adaptive Hierarchical SpatioTemporal Network for Traffic Forecasting [70.66710698485745]
  We propose an Adaptive Hierarchical SpatioTemporal Network (AHSTN) to promote traffic forecasting. AHSTN exploits the spatial hierarchy and modeling multi-scale spatial correlations. Experiments on two real-world datasets show that AHSTN achieves better performance over several strong baselines.
  arXiv  Detail & Related papers  (2023-06-15T14:50:27Z)
• 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)
• Deep Learning-Based Synchronization for Uplink NB-IoT [72.86843435313048]
  We propose a neural network (NN)-based algorithm for device detection and time of arrival (ToA) estimation for the narrowband physical random-access channel (NPRACH) of narrowband internet of things (NB-IoT) The introduced NN architecture leverages residual convolutional networks as well as knowledge of the preamble structure of the 5G New Radio (5G NR) specifications.
  arXiv  Detail & Related papers  (2022-05-22T12:16:43Z)
• Federated learning for LEO constellations via inter-HAP links [0.0]
  Low Earth Obit (LEO) satellite constellations have seen a sharp increase of deployment in recent years. To apply machine learning (ML) in such applications, the traditional way of downloading satellite data such as imagery to a ground station (GS) is not desirable. We show that existing FL solutions do not fit well in such LEO constellation scenarios because of significant challenges such as excessive convergence delay and unreliable wireless channels.
  arXiv  Detail & Related papers  (2022-05-15T08:22:52Z)
• Network Level Spatial Temporal Traffic State Forecasting with Hierarchical-Attention-LSTM (HierAttnLSTM) [0.0]
  This paper leverages diverse traffic state datasets from the Caltrans Performance Measurement System (PeMS) hosted on the open benchmark. We integrate cell and hidden states from low-level to high-level Long Short-Term Memory (LSTM) networks with an attention pooling mechanism. The developed hierarchical structure is designed to account for dependencies across different time scales, capturing the spatial-temporal correlations of network-level traffic states.
  arXiv  Detail & Related papers  (2022-01-15T05:25:03Z)

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.