AsyncFLEO: Asynchronous Federated Learning for LEO Satellite Constellations with High-Altitude Platforms

• URL: http://arxiv.org/abs/2212.11522v1
• Date: Thu, 22 Dec 2022 07:52:30 GMT
• Title: AsyncFLEO: Asynchronous Federated Learning for LEO Satellite Constellations with High-Altitude Platforms
• Authors: Mohamed Elmahallawy and Tie Luo
• Abstract summary: Federated Learning (FL) allows data to stay in-situ (never leaving satellites) FL can take several days to train a single FL model in the context of satellite communication (Satcom) We propose an asynchronous FL framework for LEO constellations called AsyncFLEO to improve FL efficiency in Satcom.
• Score: 3.096615629099617
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Low Earth Orbit (LEO) constellations, each comprising a large number of satellites, have become a new source of big data "from the sky". Downloading such data to a ground station (GS) for big data analytics demands very high bandwidth and involves large propagation delays. Federated Learning (FL) offers a promising solution because it allows data to stay in-situ (never leaving satellites) and it only needs to transmit machine learning model parameters (trained on the satellites' data). However, the conventional, synchronous FL process can take several days to train a single FL model in the context of satellite communication (Satcom), due to a bottleneck caused by straggler satellites. In this paper, we propose an asynchronous FL framework for LEO constellations called AsyncFLEO to improve FL efficiency in Satcom. Not only does AsynFLEO address the bottleneck (idle waiting) in synchronous FL, but it also solves the issue of model staleness caused by straggler satellites. AsyncFLEO utilizes high-altitude platforms (HAPs) positioned "in the sky" as parameter servers, and consists of three technical components: (1) a ring-of-stars communication topology, (2) a model propagation algorithm, and (3) a model aggregation algorithm with satellite grouping and staleness discounting. Our extensive evaluation with both IID and non-IID data shows that AsyncFLEO outperforms the state of the art by a large margin, cutting down convergence delay by 22 times and increasing accuracy by 40%.

Related papers

• 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)
• 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)
• Stragglers-Aware Low-Latency Synchronous Federated Learning via Layer-Wise Model Updates [71.81037644563217]
  Synchronous federated learning (FL) is a popular paradigm for collaborative edge learning. As some of the devices may have limited computational resources and varying availability, FL latency is highly sensitive to stragglers. We propose straggler-aware layer-wise federated learning (SALF) that leverages the optimization procedure of NNs via backpropagation to update the global model in a layer-wise fashion.
  arXiv  Detail & Related papers  (2024-03-27T09:14:36Z)
• 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)
• Secure and Efficient Federated Learning in LEO Constellations using Decentralized Key Generation and On-Orbit Model Aggregation [1.4952056744888915]
  This paper proposes FedSecure, a secure FL approach designed for LEO constellations. FedSecure preserves the privacy of each satellite's data against eavesdroppers, a curious server, or curious satellites. It also reduces convergence delay drastically from days to only a few hours, yet achieving high accuracy of up to 85.35%.
  arXiv  Detail & Related papers  (2023-09-04T21:36:46Z)
• One-Shot Federated Learning for LEO Constellations that Reduces Convergence Time from Days to 90 Minutes [3.096615629099617]
  A Low Earth orbit (LEO) satellite constellation consists of a large number of small satellites traveling in space with high mobility. Federated Learning (FL) is a promising approach because it eliminates the need for transmitting raw data and hence is both bandwidth and privacy-friendly. We propose a novel one-shot FL approach for LEO satellites, called LEOShot, that needs only a single communication round to complete the entire learning process.
  arXiv  Detail & Related papers  (2023-05-21T01:57:56Z)
• 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)
• 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)
• 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)
• 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)
• Federated Learning in the Sky: Joint Power Allocation and Scheduling with UAV Swarms [98.78553146823829]
  Unmanned aerial vehicle (UAV) swarms must exploit machine learning (ML) in order to execute various tasks. In this paper, a novel framework is proposed to implement distributed learning (FL) algorithms within a UAV swarm.
  arXiv  Detail & Related papers  (2020-02-19T14:04:01Z)

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.