Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Orchestrating Federated Learning in Space-Air-Ground Integrated Networks: Adaptive Data Offloading and Seamless Handover

Dong-Jun Han, Wenzhi Fang, Seyyedali Hosseinalipour, Mung Chiang, Christopher G. Brinton

TL;DR

This work presents a federated learning framework tailored for space-air-ground integrated networks (SAGINs) to enable ML services in remote regions lacking terrestrial infrastructure. It jointly optimizes adaptive data offloading and in-space handovers across ground, air, and space layers, treating satellites as both edge compute units and model aggregators. The authors prove convergence to a stationary point for non-convex loss and derive a latency-aware offloading strategy, validated by experiments on MNIST, FMNIST, and CIFAR-10 that show faster training and higher accuracy than baselines. The approach demonstrates how SAGIN resources can dramatically reduce FL training time while maintaining privacy guarantees for ground devices. This has practical impact for disaster response, rural autonomy, and global health data collaboration where terrestrial networks are unreliable or absent.

Abstract

Devices located in remote regions often lack coverage from well-developed terrestrial communication infrastructure. This not only prevents them from experiencing high quality communication services but also hinders the delivery of machine learning services in remote regions. In this paper, we propose a new federated learning (FL) methodology tailored to space-air-ground integrated networks (SAGINs) to tackle this issue. Our approach strategically leverages the nodes within space and air layers as both (i) edge computing units and (ii) model aggregators during the FL process, addressing the challenges that arise from the limited computation powers of ground devices and the absence of terrestrial base stations in the target region. The key idea behind our methodology is the adaptive data offloading and handover procedures that incorporate various network dynamics in SAGINs, including the mobility, heterogeneous computation powers, and inconsistent coverage times of incoming satellites. We analyze the latency of our scheme and develop an adaptive data offloading optimizer, and also characterize the theoretical convergence bound of our proposed algorithm. Experimental results confirm the advantage of our SAGIN-assisted FL methodology in terms of training time and test accuracy compared with various baselines.

Orchestrating Federated Learning in Space-Air-Ground Integrated Networks: Adaptive Data Offloading and Seamless Handover

TL;DR

This work presents a federated learning framework tailored for space-air-ground integrated networks (SAGINs) to enable ML services in remote regions lacking terrestrial infrastructure. It jointly optimizes adaptive data offloading and in-space handovers across ground, air, and space layers, treating satellites as both edge compute units and model aggregators. The authors prove convergence to a stationary point for non-convex loss and derive a latency-aware offloading strategy, validated by experiments on MNIST, FMNIST, and CIFAR-10 that show faster training and higher accuracy than baselines. The approach demonstrates how SAGIN resources can dramatically reduce FL training time while maintaining privacy guarantees for ground devices. This has practical impact for disaster response, rural autonomy, and global health data collaboration where terrestrial networks are unreliable or absent.

Abstract

Devices located in remote regions often lack coverage from well-developed terrestrial communication infrastructure. This not only prevents them from experiencing high quality communication services but also hinders the delivery of machine learning services in remote regions. In this paper, we propose a new federated learning (FL) methodology tailored to space-air-ground integrated networks (SAGINs) to tackle this issue. Our approach strategically leverages the nodes within space and air layers as both (i) edge computing units and (ii) model aggregators during the FL process, addressing the challenges that arise from the limited computation powers of ground devices and the absence of terrestrial base stations in the target region. The key idea behind our methodology is the adaptive data offloading and handover procedures that incorporate various network dynamics in SAGINs, including the mobility, heterogeneous computation powers, and inconsistent coverage times of incoming satellites. We analyze the latency of our scheme and develop an adaptive data offloading optimizer, and also characterize the theoretical convergence bound of our proposed algorithm. Experimental results confirm the advantage of our SAGIN-assisted FL methodology in terms of training time and test accuracy compared with various baselines.
Paper Structure (25 sections, 3 theorems, 52 equations, 7 figures, 2 algorithms)

This paper contains 25 sections, 3 theorems, 52 equations, 7 figures, 2 algorithms.

Table of Contents

  1. Introduction
  2. Motivation and Key Questions
  3. Main Contributions
  4. Related Works
  5. Problem Setup
  6. Methodology Overview
  7. Adaptive Inter-Layer Data Offloading
  8. Local Training at Ground and Air Layers
  9. Satellite-Side Training and Data/Model Handover
  10. Model Aggregation
  11. Adaptive Data Offloading Optimization
  12. Characterization of Data Transmission Direction
  13. Case I: Data Offloading from Space to Air/Ground
  14. Case II: Data Offloading From Air/Ground to Space
  15. Complexity Analysis
  16. ...and 10 more sections

Key Result

Theorem 1

Suppose that Assumptions assump_smooth--bound_heterogeneity hold and the learning rates satisfies where $H$ denotes the number of local iterations at each node per global round. Then under non-convex settings, our algorithm satisfies the following convergence result: where $F^*$ is the minimum value that $F(\mathbf{w})$ can achieve and $\Gamma_R = \sum_{r=0}^{R-1} \eta^{(r)}$ is the summation o

Figures (7)

  • Figure 1: Overview of adaptive data offloading/handover during FL over SAGINs, depending on the current resource availability.
  • Figure 2: Illustration of model training and intra-layer data/model handover procedures at the space layer. If the current satellite is not able to complete the task within its coverage time over the target region, the next incoming satellite continues local training after receiving the dataset and the model from the previous satellite to ensure a seamless FL process.
  • Figure 3: Illustration of the satellite constellation constructed based on the walkerStar function.
  • Figure 4: Accuracy versus training time plots. For the static optimization scheme, we apply our inter-layer data offloading scheme only in the first global round and keep the intra-layer data fixed throughout the remaining rounds. The results show the advantage of adaptive data offloading optimization considering both space and air layers.
  • Figure 5: Effect of computation capabilities of space/air nodes.
  • ...and 2 more figures

Theorems & Definitions (5)

  • Remark 1
  • Theorem 1
  • proof
  • Lemma 1
  • Lemma 2