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Federated learning (FL) is a distributed machine learning paradigm enabling collaborative model training while preserving data privacy.
C. Dwork, “Differential privacy,” in International colloquium on automata, languages, and programming . Springer, 2006, pp. 1–12
2006
Earlier work this paper cites.
P. Lindstrom, “Fixed-rate compressed floating-point arrays,” IEEE Transactions on Visualization and Computer Graphics , vol. 20, no. 12, pp. 2674–2683, 2014
2014
Earlier work this paper cites.
S. Tuecke, R. Ananthakrishnan, K. Chard, M. Lidman, B. McCollam, S. Rosen, and I. Foster, “Globus Auth: A research identity and access management platform,” in 2016 IEEE 12th International Conference on e-Science (e-Science) . IEEE, 2016, pp. 203–212
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 Artificial intelligence and statistics . PMLR, 2017, pp. 1273–1282
2017
Earlier work this paper cites.
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
A. Lalitha, S. Shekhar, T. Javidi, and F. Koushanfar, “Fully decentralized federated learning,” in Third workshop on bayesian deep learning (NeurIPS) , vol. 2, 2018
2018
Earlier work this paper cites.
X. Liang, S. Di, D. Tao, S. Li, S. Li, H. Guo, Z. Chen, and F. Cappello, “Error-controlled lossy compression optimized for high compression ratios of scientific datasets,” in 2018 IEEE International Conference on Big Data (Big Data) . IEEE, 2018, pp. 438–447
2018
Earlier work this paper cites.
G. Wang, C. X. Dang, and Z. Zhou, “Measure contribution of participants in federated learning,” in 2019 IEEE international conference on Big Data (Big Data) . IEEE, 2019, pp. 2597–2604
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
K. Bonawitz, H. Eichner, W. Grieskamp, D. Huba, A. Ingerman, V. Ivanov, C. Kiddon, J. Konečnỳ, S. Mazzocchi, B. McMahan et al. , “Towards federated learning at scale: System design,” Proceedings of Machine Learning and Systems , vol. 1, pp. 374–388, 2019
2019
Earlier work this paper cites.
C. Xie, S. Koyejo, and I. Gupta, “Zeno: Distributed stochastic gradient descent with suspicion-based fault-tolerance,” in International Conference on Machine Learning . PMLR, 2019, pp. 6893–6901
2019
Earlier work this paper cites.
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
S. P. Karimireddy, S. Kale, M. Mohri, S. Reddi, S. Stich, and A. T. Suresh, “Scaffold: Stochastic controlled averaging for federated learning,” in International conference on machine learning . PMLR, 2020, pp. 5132–5143
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
2020
Cited alongside, same era.
V. Tolpegin, S. Truex, M. E. Gursoy, and L. Liu, “Data poisoning attacks against federated learning systems,” in Computer security–ESORICs 2020: 25th European symposium on research in computer security, ESORICs 2020, guildford, UK, September 14–18, 2020, proceedings, part i 25 . Springer, 2020, pp. 480–501
2020
Cited alongside, same era.
2020
Cited alongside, same era.
M. S. H. Abad, E. Ozfatura, D. Gunduz, and O. Ercetin, “Hierarchical federated learning across heterogeneous cellular networks,” in ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2020, pp. 8866–8870
X. Liang, K. Zhao, S. Di, S. Li, R. Underwood, A. M. Gok, J. Tian, J. Deng, J. C. Calhoun, D. Tao et al. , “Sz3: A modular framework for composing prediction-based error-bounded lossy compressors,” IEEE Transactions on Big Data , vol. 9, no. 2, pp. 485–498, 2022
2022
Later among the works it cites.
J. Ogier du Terrail, S.-S. Ayed, E. Cyffers, F. Grimberg, C. He, R. Loeb, P. Mangold, T. Marchand, O. Marfoq, E. Mushtaq et al. , “FLamby: Datasets and benchmarks for cross-silo federated learning in realistic healthcare settings,” Advances in Neural Information Processing Systems , vol. 35, pp. 5315–5334, 2022
2022
Later among the works it cites.
2023
Later among the works it cites.
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2020
Cited alongside, same era.
R. Chard, Y. Babuji, Z. Li, T. Skluzacek, A. Woodard, B. Blaiszik, I. Foster, and K. Chard, “Funcx: A federated function serving fabric for science,” in Proceedings of the 29th International symposium on high-performance parallel and distributed computing , 2020, pp. 65–76
2020
Cited alongside, same era.
K. Crawford, The atlas of AI: Power, politics, and the planetary costs of artificial intelligence . Yale University Press, 2021
2021
Cited alongside, same era.
P. Kairouz, H. B. McMahan, B. Avent, A. Bellet, M. Bennis, A. N. Bhagoji, K. Bonawitz, Z. Charles, G. Cormode, R. Cummings et al. , “Advances and open problems in federated learning,” Foundations and Trends® in Machine Learning , vol. 14, no. 1–2, pp. 1–210, 2021
2021
Cited alongside, same era.
G. Kaissis, A. Ziller, J. Passerat-Palmbach, T. Ryffel, D. Usynin, A. Trask, I. Lima Jr, J. Mancuso, F. Jungmann, M.-M. Steinborn et al. , “End-to-end privacy preserving deep learning on multi-institutional medical imaging,” Nature Machine Intelligence , vol. 3, no. 6, pp. 473–484, 2021
2021
Cited alongside, same era.
Z. Chen, W. Liao, K. Hua, C. Lu, and W. Yu, “Towards asynchronous federated learning for heterogeneous edge-powered Internet of Things,” Digital Communications and Networks , vol. 7, no. 3, pp. 317–326, 2021
2021
Cited alongside, same era.
H. Yin, A. Mallya, A. Vahdat, J. M. Alvarez, J. Kautz, and P. Molchanov, “See through gradients: Image batch recovery via gradinversion,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2021, pp. 16 337–16 346
2021
Cited alongside, same era.
S. Pati, U. Baid, B. Edwards, M. Sheller, S.-H. Wang, G. A. Reina, P. Foley, A. Gruzdev, D. Karkada, C. Davatzikos et al. , “Federated learning enables big data for rare cancer boundary detection,” Nature Communications , vol. 13, no. 1, p. 7346, 2022
2022
Cited alongside, same era.
F. Lai, Y. Dai, S. Singapuram, J. Liu, X. Zhu, H. Madhyastha, and M. Chowdhury, “Fedscale: Benchmarking model and system performance of federated learning at scale,” in International conference on machine learning . PMLR, 2022, pp. 11 814–11 827
2022
Cited alongside, same era.
2023
Later among the works it cites.
2023
Later among the works it cites.
Q. Pan, H. Cao, Y. Zhu, J. Liu, and B. Li, “Contextual client selection for efficient federated learning over edge devices,” IEEE Transactions on Mobile Computing , vol. 23, no. 6, pp. 6538–6548, 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
D. Zeng, S. Liang, X. Hu, H. Wang, and Z. Xu, “FedLab: a flexible federated learning framework,” Journal of Machine Learning Research , vol. 24, no. 100, pp. 1–7, 2023
2023
Later among the works it cites.
A. Hatamizadeh, H. Yin, P. Molchanov, A. Myronenko, W. Li, P. Dogra, A. Feng, M. G. Flores, J. Kautz, D. Xu et al. , “Do gradient inversion attacks make federated learning unsafe?” IEEE Transactions on Medical Imaging , vol. 42, no. 7, pp. 2044–2056, 2023
2023
Later among the works it cites.
Z. Li, S. He, P. Chaturvedi, T.-H. Hoang, M. Ryu, E. Huerta, V. Kindratenko, J. Fuhrman, M. Giger, R. Chard et al. , “APPFLx: providing privacy-preserving cross-silo federated learning as a service,” in 2023 IEEE 19th International Conference on e-Science (e-Science) . IEEE, 2023, pp. 1–4
2023
Later among the works it cites.
J. G. Pauloski, V. Hayot-Sasson, L. Ward, N. Hudson, C. Sabino, M. Baughman, K. Chard, and I. Foster, “Accelerating Communications in Federated Applications with Transparent Object Proxies,” in Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis , ser. SC ’23. New York, NY, USA: Association for Computing Machinery, 2023. [Online]. Available: https://doi.org/10.1145/3581784.3607047
2023
Later among the works it cites.
K. Hiniduma, S. Byna, J. L. Bez, and R. Madduri, “Ai data readiness inspector (aidrin) for quantitative assessment of data readiness for ai,” in Proceedings of the 36th International Conference on Scientific and Statistical Database Management , 2024, pp. 1–12
2024
Closest in time.
Z. Li, S. He, P. Chaturvedi, V. Kindratenko, E. A. Huerta, K. Kim, and R. Madduri, “Secure federated learning across heterogeneous cloud and high-performance computing resources – a case study on federated fine-tuning of LLaMA 2,” Computing in Science & Engineering , 2024
2024
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2024
Closest in time.
G. Wilkins, S. Di, J. C. Calhoun, Z. Li, K. Kim, R. Underwood, R. Mortier, and F. Cappello, “Fedsz: Leveraging error-bounded lossy compression for federated learning communications,” in 2024 IEEE 44th International Conference on Distributed Computing Systems (ICDCS) . IEEE, 2024, pp. 577–588
2024
Closest in time.
Y. Liu, Y. Kang, T. Zou, Y. Pu, Y. He, X. Ye, Y. Ouyang, Y.-Q. Zhang, and Q. Yang, “Vertical federated learning: Concepts, advances, and challenges,” IEEE Transactions on Knowledge and Data Engineering , 2024
2024
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J. G. Pauloski, V. Hayot-Sasson, L. Ward, A. Brace, A. Bauer, K. Chard, and I. Foster, “Object Proxy Patterns for Accelerating Distributed Applications,” IEEE Transactions on Parallel and Distributed Systems , pp. 1–13, 2024
2024
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