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FedSN: A Novel Federated Learning Framework over LEO Satellite Networks

Q: How can the FedSN framework be adapted for other satellite networks or IoT devices

The FedSN framework can be adapted for other satellite networks or IoT devices by considering the specific constraints and characteristics of these networks. For satellite networks, the framework can be modified to account for different orbital periods, communication protocols, and data transmission rates. For IoT devices, the framework can be tailored to accommodate varying computing capabilities, memory constraints, and communication protocols. Additionally, the sub-structure scheme and pseudo-synchronous model aggregation methods can be adjusted to suit the specific requirements of different networks. By customizing the framework to align with the unique features of each network, it can be effectively applied to a wide range of satellite networks and IoT devices.

Q: What are the potential drawbacks or limitations of using federated learning over satellite networks

One potential drawback of using federated learning over satellite networks is the limited contact time between satellites and ground stations, which can lead to challenges in model aggregation and synchronization. The intermittent connectivity and varying orbital periods of satellites can result in model staleness and imbalanced participation in model training. Additionally, the heterogeneous computing and memory capabilities of satellites may pose challenges in ensuring uniform model training across all devices. Furthermore, the limited uplink data rate can be a bottleneck for model distribution and aggregation, impacting the overall performance of federated learning over satellite networks.

Q: How can federated learning contribute to advancements in space technology beyond model training

Federated learning can contribute to advancements in space technology beyond model training by enabling collaborative learning and data sharing among different space missions and satellite networks. By leveraging federated learning techniques, space agencies and organizations can collectively improve the accuracy and efficiency of data analysis, image recognition, and signal processing tasks. This can lead to enhanced capabilities in space exploration, satellite communication, and remote sensing applications. Additionally, federated learning can facilitate the development of intelligent autonomous systems for space missions, enabling real-time decision-making and adaptive control in dynamic space environments. Overall, federated learning has the potential to revolutionize space technology by enabling collaborative learning and knowledge sharing across diverse space systems and devices.

Kernekoncepter

FedSN proposes a novel federated learning framework to address challenges in training models over LEO satellite networks.

Resumé

The article introduces FedSN, a federated learning framework designed for Low Earth Orbit (LEO) satellite networks. It addresses challenges such as heterogeneous computing capabilities, limited uplink rates, and model staleness. The framework includes a sub-structure scheme for local model training and a pseudo-synchronous model aggregation strategy. Experiments demonstrate that FedSN outperforms existing benchmarks in terms of accuracy and overhead. The article provides insights into the design and implementation of FedSN, highlighting its key contributions and performance improvements.

Introduction to LEO satellite networks and the need for federated learning.
Challenges faced in training models over LEO satellite networks.
Proposal of FedSN framework to address these challenges.
Description of the sub-structure scheme and model aggregation strategy.
Results of experiments showcasing the effectiveness of FedSN.

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arxiv.org

Statistik

"LEO satellites always have limited connectivity with GS (e.g., 5 minutes contact time), leading to synchronous FL update failure."
"The average downlink rate is close to 100Mbps, larger than the average uplink rate with 12 Mbps."
"The size of a VGG-16 model is 528MB, and GS spends about 5.87 minutes to distribute a single model."

Citater

"FedSN achieves higher accuracy, lower computing, and communication overhead than the state-of-the-art benchmarks."
"Federated learning (FL) is a new paradigm for distributed training technology on massive participating edge devices."

Vigtigste indsigter udtrukket fra

FedSN

by Zheng Lin,Zh... kl. arxiv.org 03-28-2024

https://arxiv.org/pdf/2311.01483.pdf

Dybere Forespørgsler

How can the FedSN framework be adapted for other satellite networks or IoT devices

The FedSN framework can be adapted for other satellite networks or IoT devices by considering the specific constraints and characteristics of these networks. For satellite networks, the framework can be modified to account for different orbital periods, communication protocols, and data transmission rates. For IoT devices, the framework can be tailored to accommodate varying computing capabilities, memory constraints, and communication protocols. Additionally, the sub-structure scheme and pseudo-synchronous model aggregation methods can be adjusted to suit the specific requirements of different networks. By customizing the framework to align with the unique features of each network, it can be effectively applied to a wide range of satellite networks and IoT devices.

What are the potential drawbacks or limitations of using federated learning over satellite networks

One potential drawback of using federated learning over satellite networks is the limited contact time between satellites and ground stations, which can lead to challenges in model aggregation and synchronization. The intermittent connectivity and varying orbital periods of satellites can result in model staleness and imbalanced participation in model training. Additionally, the heterogeneous computing and memory capabilities of satellites may pose challenges in ensuring uniform model training across all devices. Furthermore, the limited uplink data rate can be a bottleneck for model distribution and aggregation, impacting the overall performance of federated learning over satellite networks.

How can federated learning contribute to advancements in space technology beyond model training

Federated learning can contribute to advancements in space technology beyond model training by enabling collaborative learning and data sharing among different space missions and satellite networks. By leveraging federated learning techniques, space agencies and organizations can collectively improve the accuracy and efficiency of data analysis, image recognition, and signal processing tasks. This can lead to enhanced capabilities in space exploration, satellite communication, and remote sensing applications. Additionally, federated learning can facilitate the development of intelligent autonomous systems for space missions, enabling real-time decision-making and adaptive control in dynamic space environments. Overall, federated learning has the potential to revolutionize space technology by enabling collaborative learning and knowledge sharing across diverse space systems and devices.