Federated Learning in Satellite Constellations

• URL: http://arxiv.org/abs/2206.00307v3
• Date: Thu, 4 May 2023 13:50:59 GMT
• Title: Federated Learning in Satellite Constellations
• Authors: Bho Matthiesen, Nasrin Razmi, Israel Leyva-Mayorga, Armin Dekorsy, Petar Popovski
• Abstract summary: Federated learning (FL) has recently emerged as a distributed machine learning paradigm for systems with limited and intermittent connectivity. This paper presents the new context brought to FL by satellite constellations, where the connectivity patterns are significantly different.
• Score: 38.58782102290874
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated learning (FL) has recently emerged as a distributed machine learning paradigm for systems with limited and intermittent connectivity. This paper presents the new context brought to FL by satellite constellations, where the connectivity patterns are significantly different from the ones observed in conventional terrestrial FL. The focus is on large constellations in low Earth orbit (LEO), where each satellites participates in a data-driven FL task using a locally stored dataset. This scenario is motivated by the trend towards mega constellations of interconnected small satellites in LEO and the integration of artificial intelligence in satellites. We propose a classification of satellite FL based on the communication capabilities of the satellites, the constellation design, and the location of the parameter server. A comprehensive overview of the current state-of-the-art in this field is provided and the unique challenges and opportunities of satellite FL are discussed. Finally, we outline several open research directions for FL in satellite constellations and present some future perspectives on this topic.

Related papers

• Space for Improvement: Navigating the Design Space for Federated Learning in Satellite Constellations [0.8437187555622164]
  We develop a method for space-ification of existing FL algorithms, evaluated on FLySTacK, our novel satellite constellation design and hardware aware testing platform. We introduce AutoFLSat, a generalized, hierarchical, autonomous FL algorithm for space that provides a 12.5% to 37.5% reduction in model training time than leading alternatives.
  arXiv  Detail & Related papers  (2024-10-31T23:49:36Z)
• Heterogeneity: An Open Challenge for Federated On-board Machine Learning [2.519319150166215]
  We present a systematic review of the challenges in the context of the cross-provider use case for Federated Learning. Such an application presents additional challenges to the Federated Learning paradigm, arising largely from the heterogeneity of such a system.
  arXiv  Detail & Related papers  (2024-08-13T13:56:17Z)
• Satellite Federated Edge Learning: Architecture Design and Convergence Analysis [47.057886812985984]
  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.
  arXiv  Detail & Related papers  (2024-04-02T11:59:58Z)
• FedSN: A Federated Learning Framework over Heterogeneous LEO Satellite Networks [18.213174641216884]
  A large number of Low Earth Orbit (LEO) satellites have been launched and deployed successfully in space by commercial companies, such as SpaceX. Due to multimodal sensors equipped by the LEO satellites, they serve not only for communication but also for various machine learning applications, such as space modulation recognition, remote sensing image classification, etc. We propose FedSN as a general FL framework to tackle the above challenges, and fully explore data diversity on LEO satellites.
  arXiv  Detail & Related papers  (2023-11-02T14:47:06Z)
• Optimizing Federated Learning in LEO Satellite Constellations via Intra-Plane Model Propagation and Sink Satellite Scheduling [3.096615629099617]
  Satellite edge computing (SEC) allows each satellite to train an ML model onboard and uploads only the model to the ground station. This paper proposes FedLEO, a novel federated learning (FL) framework that overcomes the limitation (slow convergence) of existing FL-based solutions. Our results show that FedLEO drastically expedites FL convergence, without sacrificing -- in fact it considerably increases -- the model accuracy.
  arXiv  Detail & Related papers  (2023-02-27T00:32:01Z)
• A CubeSat platform for space based quantum key distribution [62.997667081978825]
  We report on the follow-up mission of SpooQy-1, a 3U CubeSat that successfully demonstrated the generation of polarization-entangled photons in orbit. The next iteration of the mission will showcase satellite-to-ground quantum key distribution based on a compact source of polarization-entangled photon-pairs. We briefly describe the design of the optical ground station that we are currently building in Singapore for receiving the quantum signal.
  arXiv  Detail & Related papers  (2022-04-23T06:28:43Z)
• FedSpace: An Efficient Federated Learning Framework at Satellites and Ground Stations [10.250105527148731]
  Large-scale deployments of low Earth orbit (LEO) satellites collect massive amount of Earth imageries and sensor data. It is often infeasible to download all the high-resolution images and train these machine learning models on the ground because of limited downlink bandwidth, sparse connectivity, and regularization constraints on the imagery resolution. We propose Federated Learning (FL), where ground stations and satellites collaboratively train a global ML model without sharing the captured images on the satellites.
  arXiv  Detail & Related papers  (2022-02-02T20:09:27Z)
• Learning Emergent Random Access Protocol for LEO Satellite Networks [51.575090080749554]
  We propose a novel grant-free random access solution for LEO SAT networks, dubbed emergent random access channel protocol (eRACH) eRACH is a model-free approach that emerges through interaction with the non-stationary network environment. Compared to RACH, we show from various simulations that our proposed eRACH yields 54.6% higher average network throughput.
  arXiv  Detail & Related papers  (2021-12-03T07:44:45Z)
• Deep Learning Aided Routing for Space-Air-Ground Integrated Networks Relying on Real Satellite, Flight, and Shipping Data [79.96177511319713]
  Current maritime communications mainly rely on satellites having meager transmission resources, hence suffering from poorer performance than modern terrestrial wireless networks. With the growth of transcontinental air traffic, the promising concept of aeronautical ad hoc networking relying on commercial passenger airplanes is potentially capable of enhancing satellite-based maritime communications via air-to-ground and multi-hop air-to-air links. We propose space-air-ground integrated networks (SAGINs) for supporting ubiquitous maritime communications, where the low-earth-orbit satellite constellations, passenger airplanes, terrestrial base stations, ships, respectively, serve as the space-, air-,
  arXiv  Detail & Related papers  (2021-10-28T14:12:10Z)
• Integrating LEO Satellite and UAV Relaying via Reinforcement Learning for Non-Terrestrial Networks [51.05735925326235]
  A mega-constellation of low-earth orbit (LEO) satellites has the potential to enable long-range communication with low latency. We study the problem of forwarding packets between two faraway ground terminals, through an LEO satellite selected from an orbiting constellation. To maximize the end-to-end data rate, the satellite association and HAP location should be optimized. We tackle this problem using deep reinforcement learning (DRL) with a novel action dimension reduction technique.
  arXiv  Detail & Related papers  (2020-05-26T05:39:27Z)

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.