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Federated learning (FL) has been gaining attention for its ability to share knowledge while maintaining user data, protecting privacy, increasing learning efficiency, and reducing communication overhead.
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2020
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2020
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P. Kairouz, H. B. McMahan, B. Avent, A. Bellet, M. Bennis, A. N. Bhagoji, K. Bonawitz, Z. Charles, G. Cormode, R. Cummings
2021
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D. Wang, S. Shi, Y. Zhu, and Z. Han, “Federated analytics: Opportunities and challenges,”
2021
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D. Chen, D. Wang, Y. Zhu, and Z. Han, “Digital twin for federated analytics using a bayesian approach,”
2021
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S. R. Pandey, M. N. Nguyen, T. N. Dang, N. H. Tran, K. Thar, Z. Han, and C. S. Hong, “Edge-assisted democratized learning toward federated analytics,”
2021
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A. Nguyen, T. Do, M. Tran, B. X. Nguyen, C. Duong, T. Phan, E. Tjiputra, and Q. D. Tran, “Deep federated learning for autonomous driving,” in
2022
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2022
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2022
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T. Nguyen, M. Dakka, S. Diakiw, M. VerMilyea, M. Perugini, J. Hall, and D. Perugini, “A novel decentralized federated learning approach to train on globally distributed, poor quality, and protected private medical data,”
2022
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D. Froelicher, J. R. Troncoso-Pastoriza, J. L. Raisaro, M. A. Cuendet, J. S. Sousa, H. Cho, B. Berger, J. Fellay, and J.-P. Hubaux, “Truly privacy-preserving federated analytics for precision medicine with multiparty homomorphic encryption,”
2021
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L. U. Khan, W. Saad, Z. Han, E. Hossain, and C. S. Hong, “Federated learning for internet of things: Recent advances, taxonomy, and open challenges,”
2021
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B. Pfitzner, N. Steckhan, and B. Arnrich, “Federated learning in a medical context: a systematic literature review,”
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J. Li, Y. Meng, L. Ma, S. Du, H. Zhu, Q. Pei, and X. Shen, “A federated learning based privacy-preserving smart healthcare system,”
2021
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I. Dayan, H. R. Roth, A. Zhong, A. Harouni, A. Gentili, A. Z. Abidin, A. Liu, A. B. Costa, B. J. Wood, C.-S. Tsai
2021
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M. Chen, N. Shlezinger, H. V. Poor, Y. C. Eldar, and S. Cui, “Communication-efficient federated learning,”
2021
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M. Zhang, E. Wei, and R. Berry, “Faithful edge federated learning: Scalability and privacy,”
2021
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2022
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W. Qiu, W. Ai, H. Chen, Q. Feng, and G. Tang, “Decentralized federated learning for industrial IoT with deep echo state networks,”
2022
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M. Du, H. Zheng, X. Feng, Y. Chen, and T. Zhao, “Decentralized federated learning with markov chain based consensus for industrial IoT networks,”
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2022
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2022
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2022
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2022
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H. Su, C. Xiang, and B. Ramesh, “Towards confidential chatbot conversations: A decentralised federated learning framework,”
2024
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