FedHAP: Fast Federated Learning for LEO Constellations using Collaborative HAPs
Author:
Affiliation:
1. Missouri University of Science and Technology,Computer Science department,USA
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10038977/10039087/10039157.pdf?arnumber=10039157
Reference16 articles.
1. Airborne GNSS-R: A Key Enabling Technology for Environmental Monitoring
2. A hybrid chaotic blowfish encryption for high-resolution satellite imagery
3. Advances and Open Problems in Federated Learning
4. Fedspace: An efficient federated learning framework at satellites and ground stations;So;arXiv preprint,2022
5. On-Board Federated Learning for Dense LEO Constellations
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1. FedGSM: Efficient Federated Learning for LEO Constellations with Gradient Staleness Mitigation;2023 IEEE 24th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC);2023-09-25
2. One-Shot Federated Learning for LEO Constellations that Reduces Convergence Time from Days to 90 Minutes;2023 24th IEEE International Conference on Mobile Data Management (MDM);2023-07
3. Optimizing Federated Learning in LEO Satellite Constellations via Intra-Plane Model Propagation and Sink Satellite Scheduling;ICC 2023 - IEEE International Conference on Communications;2023-05-28
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