SatFed: A Resource-Efficient LEO Satellite-Assisted Heterogeneous Federated Learning Framework

A. Tirumala, “Iperf: The TCP/UDP Bandwidth Measurement Tool,” http://dast. nlanr. net/Projects/Iperf/ , 1999

1999

Earlier work this paper cites.

A. Krizhevsky, G. Hinton et al. , “Learning multiple layers of features from tiny images,” 2009

2009

Earlier work this paper cites.

P. Apollonio, C. Caini, and M. Lülf, “DTN LEO Satellite Communications Through Ground Stations and GEO Relays,” in Proc. of the 5th PSATS 2013 , 2013, p. 1–12

2013

Earlier work this paper cites.

D. P. Kingma and J. Ba, “Adam: A Method for Stochastic Optimization,” arXiv preprint arXiv:1412.6980 , Dec. 2014

Original

2014

Earlier work this paper cites.

P. Öhlén, B. Skubic, A. Rostami, M. Fiorani, P. Monti, Z. Ghebretensaé, J. Mårtensson, K. Wang, and L. Wosinska, “Data Plane and Control Architectures for 5G Transport Networks,” J. Light. Technol. , vol. 34, no. 6, pp. 1501–1508, Feb. 2016

2016

Earlier work this paper cites.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” in Proc. of the IEEE/CVF CVPR , 2016, pp. 770–778

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.

H. Xiao, K. Rasul, and R. Vollgraf, “Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms,” arXiv preprint arXiv:1708.07747 , 2017

Original

2017

Earlier work this paper cites.

V. Smith, C.-K. Chiang, M. Sanjabi, and A. S. Talwalkar, “Federated Multi-Task Learning,” in Proc. of the 31th NIPS , 2017, pp. 4427–4437

2017

Earlier work this paper cites.

L. Marelli and G. Testa, “Scrutinizing the EU General Data Protection Regulation,” Science , vol. 360, no. 6388, pp. 496–498, 2018

2018

Earlier work this paper cites.

C. Xie, S. Koyejo, and I. Gupta, “Asynchronous federated optimization,” arXiv preprint arXiv:1903.03934 , 2019

Original

2019

Earlier work this paper cites.

T. Li, A. K. Sahu, M. Zaheer, M. Sanjabi, A. Talwalkar, and V. Smith, “Federated Optimization in Heterogeneous Networks,” Proc. of the 3rd MLSys , pp. 429–450, 2020

2020

Earlier work this paper cites.

P. J. Basford, S. J. Johnston, C. S. Perkins, T. Garnock-Jones, F. P. Tso, D. Pezaros, R. D. Mullins, E. Yoneki, J. Singer, and S. J. Cox, “Performance Analysis of Single Board Computer Clusters,” Future Gener. Comput. Syst. , vol. 102, pp. 278–291, 2020

2020

Earlier work this paper cites.

E. Diao, J. Ding, and V. Tarokh, “Heterofl: Computation and Communication Efficient Federated Learning for Heterogeneous Clients,” arXiv preprint arXiv:2010.01264 , Oct. 2020

Original

2020

Earlier work this paper cites.

Z. Lin, L. Wang, B. Tan, and X. Li, “Spatial-spectral terahertz networks,” IEEE Trans. Wirel. Commun. , vol. 21, no. 6, pp. 3881–3892, 2021

2021

Earlier work this paper cites.

A. Sacco, M. Flocco, F. Esposito, and G. Marchetto, “Owl: Congestion Control with Partially Invisible Networks via Reinforcement Learning,” in Proc. of the 40th IEEE INFOCOM , 2021, pp. 1–10

2021

Earlier work this paper cites.

T. Li, S. Hu, A. Beirami, and V. Smith, “Ditto: Fair and Robust Federated Learning through Personalization,” in Proc. of the 38th ICML , 2021, pp. 6357–6368

2021

Earlier work this paper cites.

Z. Wang, H. Xu, J. Liu, H. Huang, C. Qiao, and Y. Zhao, “Resource-Efficient Federated Learning with Hierarchical Aggregation in Edge Computing,” in Proc. of the 40th IEEE INFOCOM , 2021, pp. 1–10

2021

Earlier work this paper cites.

C. Wu, J. Chen, K. He, Z. Zhao, R. Du, and C. Zhang, “Echohand: High accuracy and presentation attack resistant hand authentication on commodity mobile devices,” in Proc. CCS , 2022, pp. 2931–2945

2022

Earlier work this paper cites.

J. Ren, J. Liu, Y. Zhang, Z. Li, F. Lyu, Z. Wang, and Y. Zhang, “An efficient two-layer task offloading scheme for mec system with multiple services providers,” in Proc. of the 41st IEEE INFOCOM , 2022, pp. 1519–1528

2022

Earlier work this paper cites.

Z. Lin, L. Wang, J. Ding, B. Tan, and S. Jin, “Channel power gain estimation for terahertz vehicle-to-infrastructure networks,” IEEE Commun. Lett. , vol. 27, no. 1, pp. 155–159, 2022

2022

Earlier work this paper cites.

Y. Zhang, Q. Wu, Z. Lai, and H. Li, “Enabling Low-Latency-Capable Satellite-Ground Topology for Emerging Leo Satellite Networks,” in Proc. of the 41st IEEE INFOCOM , 2022, pp. 1329–1338

2022

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.

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.

A. Z. Tan, H. Yu, L. Cui, and Q. Yang, “Towards Personalized Federated Learning,” IEEE Trans. Neural Netw. Learn. Syst. , vol. 34, no. 12, pp. 9587–9603, Mar. 2022

2022

Cited alongside, same era.

J. Liu, J. Ren, Y. Zhang, S. Yue, and Y. Zhang, “SESAME: A Resource Expansion and Sharing Scheme for Multiple Edge Services Providers,” IEEE/ACM Trans. Netw. , pp. 1–16, 2024

2024

Closest in time.

C. Wu, J. Chen, Q. Fang, K. He, Z. Zhao, H. Ren, G. Xu, Y. Liu, and Y. Xiang, “Rethinking membership inference attacks against transfer learning,” IEEE Trans. Inf. Forensics Secur. , 2024

2024

Closest in time.

J. Huang, K. Yuan, C. Huang, and K. Huang, “D 2

Original

2024

Closest in time.

Z. Fang, S. Hu, H. An, Y. Zhang, J. Wang, H. Cao, X. Chen, and Y. Fang, “Pacp: Priority-aware collaborative perception for connected and autonomous vehicles,” IEEE Trans. Mob. Comput. , 2024

2024

Closest in time.