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Recently, the increasing deployment of LEO satellite systems has enabled various space analytics (e.g., crop and climate monitoring), which heavily relies on the advancements in deep learning (DL).
A. Tirumala, “Iperf: The TCP/UDP Bandwidth Measurement Tool,” http://dast. nlanr. net/Projects/Iperf/ , 1999
1999
Earlier work this paper cites.
M. S. Monmonier, Spying with Maps: Surveillance Technologies and the Future of Privacy . University of Chicago Press, 2004
2004
Earlier work this paper cites.
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A Simple Way to Prevent Neural Networks from Overfitting,” J. Mach. Learn. Res. , vol. 15, no. 1, pp. 1929–1958, 2014
2014
Earlier work this paper cites.
K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-scale Image Recognition,” in Proc. of the 3rd ICLR , 2015
2015
Earlier work this paper cites.
M. Casoni, C. A. Grazia, M. Klapez, N. Patriciello, A. Amditis, and E. Sdongos, “Integration of Satellite and LTE for Disaster Recovery,” IEEE Commun. Mag. , vol. 53, no. 3, pp. 47–53, 2015
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
B. Pfaff, J. Pettit, T. Koponen, E. Jackson, A. Zhou, J. Rajahalme, J. Gross, A. Wang, J. Stringer, P. Shelar et al. , “The Design and Implementation of Open vSwitch,” in Proc. of th 12th NSDI , 2015, pp. 117–130
2015
Earlier work this paper cites.
J. D. Beshay, A. Francini, and R. Prakash, “On the Fidelity of Single-Machine Network Emulation in Linux,” in Proc. of the 23rd IEEE MASCOTS , 2015, pp. 19–22
2015
Earlier work this paper cites.
2016
Earlier work this paper cites.
I. Goodfellow, Deep Learning . MIT press, 2016, vol. 196
2016
Earlier work this paper cites.
B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, “Communication-efficient Learning of Deep Networks from Decentralized Data,” in Proc. of the 20th AISTATS , 2017, pp. 1273–1282
2017
Earlier work this paper cites.
A. Tarvainen and H. Valpola, “Mean Teachers Are Better Role Models: Weight-averaged Consistency Targets Improve Semi-supervised Deep Learning Results,” in Proc. of the 31st NIPS , 2017, pp. 1195 – 1204
2017
Earlier work this paper cites.
B. Aragon, R. Houborg, K. Tu, J. B. Fisher, and M. McCabe, “CubeSats Enable High Spatiotemporal Retrievals of Crop-water Use for Precision Agriculture,” Remote Sens. , vol. 10, no. 12, p. 1867, 2018
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
D. Berthelot, N. Carlini, I. Goodfellow, N. Papernot, A. Oliver, and C. A. Raffel, “Mixmatch: A Holistic Approach to Semi-supervised Learning,” in Proc. of the 33rd NIPS , 2019, pp. 5049 – 5059
2019
Earlier work this paper cites.
K. Devaraj, M. Ligon, E. Blossom, J. Breu, B. Klofas, K. Colton, and R. W. Kingsbury, “Planet High Speed Radio: Crossing Gbps from a 3U CubeSat,” in Proc. of the 33rd Small Satellite Conference , 2019, pp. 1–10
2019
Earlier work this paper cites.
P. Helber, B. Bischke, A. Dengel, and D. Borth, “EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification,” IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens. , vol. 12, no. 7, pp. 2217–2226, 2019
2019
Earlier work this paper cites.
I. Franch-Pardo, B. M. Napoletano, F. Rosete-Verges, and L. Billa, “Spatial Analysis and GIS in the Study of COVID-19. A Review,” Sci. Total Environ. , vol. 739, p. 140033, 2020
2020
Earlier work this paper cites.
M. M. Coffer, “Balancing Privacy Rights and the Production of High-Quality Satellite Imagery,” 2020
2020
Earlier work this paper cites.
K. Sohn, D. Berthelot, N. Carlini, Z. Zhang, H. Zhang, C. A. Raffel, E. D. Cubuk, A. Kurakin, and C.-L. Li, “Fixmatch: Simplifying Semi-supervised Learning with Consistency and Confidence,” in Proc. of the 34th NIPS , 2020, pp. 596 – 608
2020
Earlier work this paper cites.
D. Berthelot, N. Carlini, E. D. Cubuk, A. Kurakin, K. Sohn, H. Zhang, and C. Raffel, “Remixmatch: Semi-supervised Learning with Distribution Alignment and Augmentation Anchoring,” in Proc. of the 8th ICLR , 2020
2020
Earlier work this paper cites.
Q. Xie, Z. Dai, E. Hovy, T. Luong, and Q. Le, “Unsupervised Data Augmentation for Consistency Training,” in Proc. of the 34th NIPS , 2020, pp. 6256 – 6268
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
C.-W. Kuo, C.-Y. Ma, J.-B. Huang, and Z. Kira, “Featmatch: Feature-based augmentation for semi-supervised learning,” in Proc. of the 16th ECCV , 2020, pp. 479–495
2020
Earlier work this paper cites.
Z. Wang, W. Bai, M. Sheng, J. Li, R. Liu, and Y. Bi, “Exploiting Mobile Carrying to Improve the Capacity of Satellite Networks,” in Proc. of the VTC , Apr. 2021, pp. 1–6
2021
Earlier work this paper cites.
S. Shukla, D. Macharia, G. J. Husak, M. Landsfeld, C. L. Nakalembe, S. L. Blakeley, E. C. Adams, and J. Way-Henthorne, “Enhancing Access and Usage of Earth Observations in Environmental Decision-making in Eastern and Southern Africa Through Capacity Building,” Front. Sustainable Food Syst. , vol. 5, p. 504063, 2021
2021
Earlier work this paper cites.
A. Li, J. Sun, P. Li, Y. Pu, H. Li, and Y. Chen, “Hermes: An Efficient Federated Learning Framework for Heterogeneous Mobile Clients,” in Proc. of the 27th ACM MobiCom , 2021, pp. 420–437
2021
Cited alongside, same era.
D. Vasisht, J. Shenoy, and R. Chandra, “L2D2: Low Latency Distributed Downlink for LEO Satellites,” in Proc. of the 35th ACM SIGCOMM , 2021, pp. 151–164
2021
Cited alongside, same era.
T. Ahmmed, A. Alidadi, Z. Zhang, A. U. Chaudhry, and H. Yanikomeroglu, “The Digital Divide in Canada and the Role of LEO Satellites in Bridging the Gap,” IEEE Commun. Mag. , vol. 60, no. 6, pp. 24–30, 2022
2022
Cited alongside, same era.
V. Singh, O. Yağan, and S. Kumar, “Selfiestick: Towards Earth Imaging from a Low-Cost Ground Module Using LEO Satellites,” in Proc. of the 21st ACM/IEEE IPSN , 2022, pp. 220–232
2022
Cited alongside, same era.
(2024) “Planet”. Available: https://www.planet.com/
2024
Later among the works it cites.
Z. Lin, Z. Chen, Z. Fang, X. Chen, X. Wang, and Y. Gao, “FedSN: A Federated Learning Framework over Heterogeneous LEO Satellite Networks,” IEEE Trans. Mobile Comput. , Oct. 2024
2024
Later among the works it cites.
2024
Later among the works it cites.
V. Singh, T. Chakraborty, S. Jog, O. Chabra, D. Vasisht, and R. Chandra, “Spectrumize: Spectrum-Efficient Satellite Networks for the Internet of Things,” in Proc. of the 21st NSDI , 2024, pp. 825–840
2024
Later among the works it cites.
J. Peng, Z. Chen, Z. Lin, H. Yuan, Z. Fang, L. Bao, Z. Song, Y. Li, J. Ren, and Y. Gao, “Sums: Sniffing Unknown Multiband Signals under Low Sampling Rates,” IEEE Trans. Mobile Comput. , Nov. 2024
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W. Huang, M. Ye, and B. Du, “Learn From Others And Be Yourself in Heterogeneous Federated Learning,” in Proc. of the 35th IEEE/CVF CVPR , 2022, pp. 10 143–10 153
2022
Cited alongside, same era.
L. Qu, Y. Zhou, P. P. Liang, Y. Xia, F. Wang, E. Adeli, L. Fei-Fei, and D. Rubin, “Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learning,” in Proc. of the 35th IEEE/CVF CVPR , 2022, pp. 10 061–10 071
2022
Cited alongside, same era.
C. Thapa, P. C. M. Arachchige, S. Camtepe, and L. Sun, “Splitfed: When Federated Learning Meets Split Learning,” in Proc. of the 36th AAAI , 2022, pp. 8485–8493
2022
Cited alongside, same era.
(2022) “Deep Sub-Nyquist Modulation Recognition Challenge”. Available: http://www.gbsense.net/challenge/
2022
Cited alongside, same era.
S. Chen and B. Li, “Towards Optimal Multi-Modal Federated Learning on Non-IID Data with Hierarchical Gradient Blending,” in Proc. of the 41st IEEE INFOCOM , 2022, pp. 1469–1478
2022
Cited alongside, same era.
N. Jardak and Q. Jault, “The Potential of LEO Satellite-Based Opportunistic Navigation for High Dynamic Applications,” Sensors , vol. 22, no. 7, p. 2541, 2022
2022
Cited alongside, same era.
K. Pillutla, K. Malik, A.-R. Mohamed, M. Rabbat, M. Sanjabi, and L. Xiao, “Federated Learning With Partial Model Personalization,” in Proc. of the 39th ICML , 2022, pp. 17 716–17 758
2022
Cited alongside, same era.
(2022) “How iPhone Speeds have Grown in the Last 5 Years”. Available: https://appleinsider.com/articles/22/09/26/how-iphone-speeds-have-grown-in-the-last-5-years
2022
Cited alongside, same era.
2024
Later among the works it cites.
J. Huang, K. Yuan, C. Huang, and K. Huang, “D 2
2024
Later among the works it cites.
Z. Lin, G. Zhu, Y. Deng, X. Chen, Y. Gao, K. Huang, and Y. Fang, “Efficient Parallel Split Learning over Resource-constrained Wireless Edge Networks,” IEEE Trans. Mobile Comput. , pp. 1–16, 2024
2024
Later among the works it cites.
J. Huang, D. Li, C. Huang, X. Qin, and W. Zhang, “Joint Task and Data-Oriented Semantic Communications: A Deep Separate Source-Channel Coding Scheme,” IEEE Internet Things J. , vol. 11, no. 2, pp. 2255–2272, Jan. 2024
2024
Later among the works it cites.
(2024) “Planet Labs PBC Announces Real-Time Insights Technology Using NVIDIA Jetson Platform”. Available: https://www.businesswire.com/news/home/20240610385569/en/Planet-Labs-PBC-Announces-Real-Time-Insights-Technology-Using-NVIDIA-Jetson-Platform
2024
Later among the works it cites.
2024
Later among the works it cites.
M. Hu, J. Zhang, X. Wang, S. Liu, and Z. Lin, “Accelerating Federated Learning With Model Segmentation for Edge Networks,” IEEE Trans. Green Commun. Netw. , Jul. 2024
2024
Later among the works it cites.
Z. Wang, K. Huang, and Y. C. Eldar, “Spectrum Breathing: Protecting Over-the-Air Federated Learning Against Interference,” IEEE Trans. Wireless Commun. , vol. 23, no. 8, pp. 10 058–10 071, Feb. 2024
2024
Later among the works it cites.
2024
Later among the works it cites.
2024
Later among the works it cites.
Z. Lin, G. Qu, X. Chen, and K. Huang, “Split Learning in 6G Edge Networks,” IEEE Wireless Commun. , May. 2024
2024
Later among the works it cites.
2024
Later among the works it cites.
(2024) “NORAD GP Element Sets”. Available: https://celestrak.org/NORAD/elements/
2024
Later among the works it cites.
Z. Zhai, Q. Wu, S. Yu, R. Li, F. Zhang, and X. Chen, “FedLEO: An Offloading-assisted Decentralized Federated Learning Framework for Low Earth Orbit Satellite Networks,” IEEE Trans. Mobile Comput. , vol. 23, no. 5, pp. 5260–5279, 2024
2024
Later among the works it cites.
J. Liu, Q. Zeng, H. Xu, Y. Xu, Z. Wang, and H. Huang, “Adaptive Block-Wise Regularization and Knowledge Distillation for Enhancing Federated Learning,” IEEE/ACM Trans. Netw. , vol. 32, no. 1, pp. 791–805, 2024
2024
Later among the works it cites.
M. Elmahallawy, T. Luo, and K. Ramadan, “Communication-efficient Federated Learning for LEO Satellite Networks Integrated with HAPs Using Hybrid NOMA-OFDM,” IEEE J. Select. Areas Commun. , vol. 42, no. 5, pp. 1097–1114, 2024
2024
Later among the works it cites.
2024
Later among the works it cites.
2024
Later among the works it cites.
2024
Later among the works it cites.
2024
Later among the works it cites.
P. Hu, Y. Qian, T. Zheng, A. Li, Z. Chen, Y. Gao, X. Cheng, and J. Luo, “t-READi: Transformer-Powered Robust and Efficient Multimodal Inference for Autonomous Driving,” IEEE Trans. Mobile Comput. , Sep. 2024
2024
Later among the works it cites.