[1] |
van Engelen J E, Hoos H H. A survey on semi-supervised learning. Machine Learning, 2020, 109: 373–440. doi: 10.1007/s10994-019-05855-6
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[2] |
Ang J C, Mirzal A, Haron H, et al. Supervised, unsupervised, and semi-supervised feature selection: A review on gene selection. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2016, 13 (5): 971–989. doi: 10.1109/tcbb.2015.2478454
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[3] |
Zhu X, Goldberg A B. Introduction to Semi-Supervised Learning. Cham, Switzerland: Springer, 2009.
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[4] |
Scudder H. Probability of error of some adaptive pattern-recognition machines. IEEE Transactions on Information Theory, 1965, 11 (3): 363–371. doi: 10.1109/tit.1965.1053799
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[5] |
Blum A, Mitchell T. Combining labeled and unlabeled data with co-training. In: COLT' 98: Proceedings of the Eleventh Annual Conference on Computational Learning Theory. New York: ACM, 1998: 92–100.
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[6] |
Belkin M, Niyogi P, Sindhwani V. Manifold regularization: A geometric framework for learning from labeled and unlabeled examples. Journal of Machine Learning Research, 2006, 7: 2399–2434. doi: 10.5555/1248547.1248632
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[7] |
Belkin M, Niyogi P. Semi-supervised learning on Riemannian manifolds. Machine Learning, 2004, 56: 209–239. doi: 10.1023/B:MACH.0000033120.25363.1e
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Chapelle O, Schölkopf B, Zien A, Transductive support vector machines. In: Semi-Supervised Learning. Cambridge: MIT Press. 2006, 105–117.
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Chong Y, Ding Y, Yan Q, et al. Graph-based semi-supervised learning: A review. Neurocomputing, 2020, 408 (30): 216–230. doi: 10.1016/j.neucom.2019.12.130
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[10] |
Zhu X, Ghahramani Z, Lafferty J. Semi-supervised learning using Gaussian fields and harmonic functions. In: ICML'03: Proceedings of the Twentieth International Conference on International Conference on Machine Learning. Washington, DC: AAAI Press, 2003: 912–919.
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Zhou D, Bousquet O, Lal T N, et al. Learning with local and global consistency. In: NIPS'03: Proceedings of the 16th International Conference on Neural Information Processing Systems. New York: ACM, 2003: 321–328.
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[12] |
Chen J, Wang C, Sun Y, et al. Semi-supervised Laplacian regularized least squares algorithm for localization in wireless sensor networks. Computer Networks, 2011, 55 (10): 2481–2491. doi: 10.1016/j.comnet.2011.04.010
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[13] |
Szummer M, Jaakkola T. Partially labeled classification with Markov random walks. In: NIPS'01: Proceedings of the 14th International Conference on Neural Information Processing Systems: Natural and Synthetic. Cambridge: MIT Press, 2001: 945–952.
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[14] |
Grira N, Crucianu M, Boujemaa N. Active semi-supervised fuzzy clustering for image database categorization. In: MIR '05: Proceedings of the 7th ACM SIGMM International Workshop on Multimedia Information Retrieval. New York: ACM, 2005: 9–16.
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[15] |
Chapelle O, Zien A. Semi-supervised classification by low density separation. In: AISTATS 2005–Proceedings of the 10th International Workshop on Artificial Intelligence and Statistics. Stuttgart, Germany: Max-Planck-Gesellschaft, 2005: 57–64.
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[16] |
Kostopoulos G, Karlos S, Kotsiantis S, et al. Semi-supervised regression: A recent review. Journal of Intelligent & Fuzzy Systems, 2018, 35 (2): 1483–1500. doi: 10.3233/JIFS-169689
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[17] |
Torii M, Wagholikar K, Liu H. Using machine learning for concept extraction on clinical documents from multiple data sources. Journal of the American Medical Informatics Association, 2011, 18: 580–587 . doi: 10.1136/amiajnl-2011-000155
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[18] |
Scardapane S, Fierimonte R, Wang D, et al. Distributed music classification using random vector functional-link nets. In: 2015 International Joint Conference on Neural Networks (IJCNN). Killarney, Ireland: IEEE, 2015: 1–8.
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[19] |
Shih T K, Distributed multimedia databases In: Shih T K, editor. Distributed Multimedia Databases: Techniques and Applications. Hershey, PA: IGI Global, 2002: 2–12.
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[20] |
Shen P, Du X, Li C. Distributed semi-supervised metric learning. IEEE Access, 2016, 4: 8558–8571. doi: 10.1109/ACCESS.2016.2632158
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[21] |
Scardapane S, Fierimonte R, Di Lorenzo P, et al. Distributed semi-supervised support vector machines. Neural Networks, 2016, 80: 43–52 . doi: 10.1016/j.neunet.2016.04.007
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[22] |
Fierimonte R, Scardapane S, Uncini A, et al. Fully decentralized semi-supervised learning via privacy-preserving matrix completion. IEEE Transactions on Neural Networks and Learning Systems, 2017, 28: 2699–2711. doi: 10.1109/TNNLS.2016.2597444
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[23] |
Gan H, Li Z, Wu W, et al. Safety-aware graph-based semi-supervised learning. Expert Systems With Applications, 2018, 107: 243–254. doi: 10.1016/j.eswa.2018.04.031
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[24] |
Lee K, Lam M, Pedarsani R, et al. Speeding up distributed machine learning using codes. IEEE Transactions on Information Theory, 2018, 64 (3): 1514–1529. doi: 10.1109/tit.2017.2736066
|
[25] |
Chen L, Han K, Du Y, et al. Block-division-based wireless coded computation. IEEE Wireless Communications Letters, 2022, 11 (2): 283–287. doi: 10.1109/LWC.2021.3125983
|
[26] |
Agarwal A, Duchi J C. Distributed delayed stochastic optimization. In: 2012 IEEE 51st IEEE Conference on Decision and Control (CDC). Maui, USA: IEEE, 2012: 5451–5452.
|
[27] |
Alfakih A Y, Khandani A K, Wolkowicz H. Solving euclidean distance matrix completion problems via semidefinite programming. Computational Optimization and Applications, 1999, 12: 13–30. doi: 10.1023/A:1008655427845
|
[28] |
Al-Homidan S, Wolkowicz H. Approximate and exact completion problems for Euclidean distance matrices using semidefinite programming. Linear Algebra and Its Applications, 2005, 406: 109–141. doi: 10.1016/j.laa.2005.03.021
|
[29] |
Liu W, Chen L, Zhang W. Decentralized federated learning: Balancing communication and computing costs. IEEE Transactions on Signal and Information Processing Over Networks, 2022, 8: 131–143. doi: 10.1109/TSIPN.2022.3151242
|
[30] |
Liu W, Chen L, Chen Y, et al. Accelerating federated learning via momentum gradient descent. IEEE Transactions on Parallel and Distributed Systems, 2020, 31 (8): 1754–1766. doi: 10.1109/TPDS.2020.2975189
|
[31] |
Wang Z, Du Y, Wei K, et al. Vision, application scenarios, and key technology trends for 6G mobile communications. Science China Information Sciences, 2022, 65: 151301. doi: 10.1007/s11432-021-3351-5
|
Figure
1.
Illustration of the distributed SSL setup with N client and 1 server. Each client owns a labeled data set
[1] |
van Engelen J E, Hoos H H. A survey on semi-supervised learning. Machine Learning, 2020, 109: 373–440. doi: 10.1007/s10994-019-05855-6
|
[2] |
Ang J C, Mirzal A, Haron H, et al. Supervised, unsupervised, and semi-supervised feature selection: A review on gene selection. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2016, 13 (5): 971–989. doi: 10.1109/tcbb.2015.2478454
|
[3] |
Zhu X, Goldberg A B. Introduction to Semi-Supervised Learning. Cham, Switzerland: Springer, 2009.
|
[4] |
Scudder H. Probability of error of some adaptive pattern-recognition machines. IEEE Transactions on Information Theory, 1965, 11 (3): 363–371. doi: 10.1109/tit.1965.1053799
|
[5] |
Blum A, Mitchell T. Combining labeled and unlabeled data with co-training. In: COLT' 98: Proceedings of the Eleventh Annual Conference on Computational Learning Theory. New York: ACM, 1998: 92–100.
|
[6] |
Belkin M, Niyogi P, Sindhwani V. Manifold regularization: A geometric framework for learning from labeled and unlabeled examples. Journal of Machine Learning Research, 2006, 7: 2399–2434. doi: 10.5555/1248547.1248632
|
[7] |
Belkin M, Niyogi P. Semi-supervised learning on Riemannian manifolds. Machine Learning, 2004, 56: 209–239. doi: 10.1023/B:MACH.0000033120.25363.1e
|
[8] |
Chapelle O, Schölkopf B, Zien A, Transductive support vector machines. In: Semi-Supervised Learning. Cambridge: MIT Press. 2006, 105–117.
|
[9] |
Chong Y, Ding Y, Yan Q, et al. Graph-based semi-supervised learning: A review. Neurocomputing, 2020, 408 (30): 216–230. doi: 10.1016/j.neucom.2019.12.130
|
[10] |
Zhu X, Ghahramani Z, Lafferty J. Semi-supervised learning using Gaussian fields and harmonic functions. In: ICML'03: Proceedings of the Twentieth International Conference on International Conference on Machine Learning. Washington, DC: AAAI Press, 2003: 912–919.
|
[11] |
Zhou D, Bousquet O, Lal T N, et al. Learning with local and global consistency. In: NIPS'03: Proceedings of the 16th International Conference on Neural Information Processing Systems. New York: ACM, 2003: 321–328.
|
[12] |
Chen J, Wang C, Sun Y, et al. Semi-supervised Laplacian regularized least squares algorithm for localization in wireless sensor networks. Computer Networks, 2011, 55 (10): 2481–2491. doi: 10.1016/j.comnet.2011.04.010
|
[13] |
Szummer M, Jaakkola T. Partially labeled classification with Markov random walks. In: NIPS'01: Proceedings of the 14th International Conference on Neural Information Processing Systems: Natural and Synthetic. Cambridge: MIT Press, 2001: 945–952.
|
[14] |
Grira N, Crucianu M, Boujemaa N. Active semi-supervised fuzzy clustering for image database categorization. In: MIR '05: Proceedings of the 7th ACM SIGMM International Workshop on Multimedia Information Retrieval. New York: ACM, 2005: 9–16.
|
[15] |
Chapelle O, Zien A. Semi-supervised classification by low density separation. In: AISTATS 2005–Proceedings of the 10th International Workshop on Artificial Intelligence and Statistics. Stuttgart, Germany: Max-Planck-Gesellschaft, 2005: 57–64.
|
[16] |
Kostopoulos G, Karlos S, Kotsiantis S, et al. Semi-supervised regression: A recent review. Journal of Intelligent & Fuzzy Systems, 2018, 35 (2): 1483–1500. doi: 10.3233/JIFS-169689
|
[17] |
Torii M, Wagholikar K, Liu H. Using machine learning for concept extraction on clinical documents from multiple data sources. Journal of the American Medical Informatics Association, 2011, 18: 580–587 . doi: 10.1136/amiajnl-2011-000155
|
[18] |
Scardapane S, Fierimonte R, Wang D, et al. Distributed music classification using random vector functional-link nets. In: 2015 International Joint Conference on Neural Networks (IJCNN). Killarney, Ireland: IEEE, 2015: 1–8.
|
[19] |
Shih T K, Distributed multimedia databases In: Shih T K, editor. Distributed Multimedia Databases: Techniques and Applications. Hershey, PA: IGI Global, 2002: 2–12.
|
[20] |
Shen P, Du X, Li C. Distributed semi-supervised metric learning. IEEE Access, 2016, 4: 8558–8571. doi: 10.1109/ACCESS.2016.2632158
|
[21] |
Scardapane S, Fierimonte R, Di Lorenzo P, et al. Distributed semi-supervised support vector machines. Neural Networks, 2016, 80: 43–52 . doi: 10.1016/j.neunet.2016.04.007
|
[22] |
Fierimonte R, Scardapane S, Uncini A, et al. Fully decentralized semi-supervised learning via privacy-preserving matrix completion. IEEE Transactions on Neural Networks and Learning Systems, 2017, 28: 2699–2711. doi: 10.1109/TNNLS.2016.2597444
|
[23] |
Gan H, Li Z, Wu W, et al. Safety-aware graph-based semi-supervised learning. Expert Systems With Applications, 2018, 107: 243–254. doi: 10.1016/j.eswa.2018.04.031
|
[24] |
Lee K, Lam M, Pedarsani R, et al. Speeding up distributed machine learning using codes. IEEE Transactions on Information Theory, 2018, 64 (3): 1514–1529. doi: 10.1109/tit.2017.2736066
|
[25] |
Chen L, Han K, Du Y, et al. Block-division-based wireless coded computation. IEEE Wireless Communications Letters, 2022, 11 (2): 283–287. doi: 10.1109/LWC.2021.3125983
|
[26] |
Agarwal A, Duchi J C. Distributed delayed stochastic optimization. In: 2012 IEEE 51st IEEE Conference on Decision and Control (CDC). Maui, USA: IEEE, 2012: 5451–5452.
|
[27] |
Alfakih A Y, Khandani A K, Wolkowicz H. Solving euclidean distance matrix completion problems via semidefinite programming. Computational Optimization and Applications, 1999, 12: 13–30. doi: 10.1023/A:1008655427845
|
[28] |
Al-Homidan S, Wolkowicz H. Approximate and exact completion problems for Euclidean distance matrices using semidefinite programming. Linear Algebra and Its Applications, 2005, 406: 109–141. doi: 10.1016/j.laa.2005.03.021
|
[29] |
Liu W, Chen L, Zhang W. Decentralized federated learning: Balancing communication and computing costs. IEEE Transactions on Signal and Information Processing Over Networks, 2022, 8: 131–143. doi: 10.1109/TSIPN.2022.3151242
|
[30] |
Liu W, Chen L, Chen Y, et al. Accelerating federated learning via momentum gradient descent. IEEE Transactions on Parallel and Distributed Systems, 2020, 31 (8): 1754–1766. doi: 10.1109/TPDS.2020.2975189
|
[31] |
Wang Z, Du Y, Wei K, et al. Vision, application scenarios, and key technology trends for 6G mobile communications. Science China Information Sciences, 2022, 65: 151301. doi: 10.1007/s11432-021-3351-5
|