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仅少量回传模型参数即可达到与回收模型相同的收敛效果,以大幅降低通信量.
References:
[1] Subramaniyaswamy V, Logesh R, Indragandhi V. Intelligent sports commentary recommendation system for individual cricket
players. Int’l Journal of Advanced Intelligence Paradigms, 2018,10(1-2):103−117.
[2] Manogaran G, Varatharajan R, Priyan MK. Hybrid recommendation system for heart disease diagnosis based on multiple kernel
learning with adaptive neuro-fuzzy inference system. Multimedia Tools and Applications, 2018,77(4):4379−4399.
[3] Shin H, Kim S, Shin J, et al. Privacy enhanced matrix factorization for recommendation with local differential privacy. IEEE Trans.
on Knowledge and Data Engineering, 2018,30(9):1770−1782.
[4] McMahan B, Moore E, Ramage D, et al. Communication-efficient learning of deep networks from decentralized data. In: Proc. of
the 20th Int’l Conf. on Artificial Intelligence and Statistics. Fort Lauderdale: PMLR, 2017. 1273−1282.
[5] Bonawitz K, Ivanov V, Kreuter B, et al. Practical secure aggregation for privacy-preserving machine learning. In: Proc. of the 2017
ACM SIGSAC Conf. on Computer and Communications Security. Dallas: ACM, 2017. 1175−1191.
[6] Nasr M, Shokri R, Houmansadr A. Comprehensive privacy analysis of deep learning: Stand-alone and federated learning under
passive and active white-box inference attacks. In: Proc. of the IEEE Symp. on Security and Privacy (SP). San Francisco: IEEE,
2019. 739−753.
[7] Radio Spectrum Policy Group. RSPG report on the results of the public consultation on the Reviewof the EU Telecommunications
Framework. Technical Report, 2016. http://spectrum.welter.fr/international/rspg/reports/rspg-report-2016-framework-review.pdf
[8] Huang K, Zhu G, You C, et al. Communication, computing, and learning on the edge. In: Proc. of the IEEE Int’l Conf. on
Communication Systems (ICCS). Chengdu: IEEE, 2018. 268−273.
[9] Song X, Feng F, Han X, et al. Neural compatibility modeling with attentive knowledge distillation. In: Proc. of the 41st Int’l ACM
SIGIR Conf. on Research and Development in Information Retrieval. New York: ACM, 2018. 5−14.
[10] Jeong E, Oh S, Kim H. Communication-efficient on-device machine learning: Federated distillation and augmentation under non-
IID private data. In: Proc. of the 2nd Workshop on Machine Learning on the Phone and other Consumer Devices (MLPCD 2).
Montréal: JMLR, 2018.
[11] Luo L, Huang W, Zeng Q. Learning personalized end-to-end goal-oriented dialog. In: Proc.of the AAAI Conf. on Artificial
Intelligence, Vol.33. Honolulu: AAAI, 2019. 6794−6801.
[12] Yang Q, Liu Y, Chen T, et al. Federated machine learning: Concept and applications. ACM Trans. on Intelligent Systems and
Technology (TIST), 2019,10(2):1−19.
[13] Li T, Sahu AK, Talwalkar A, et al. Federated learning: Challenges, methods, and future directions. IEEE Signal Processing
Magazine, 2020,37(3):50−60.
[14] Smith V, Chiang CK, Sanjabi M. Federated multi-task learning. In: Proc. of the Advances in Neural Information Processing
Systems (NIPS). Long Beach: Curran Associates, Inc., 2017. 4424−4434.
[15] Gao D, Liu Y, Huang A, et al. Privacy-preserving heterogeneous federated transfer learning. In: Proc. of the 2019 IEEE Int’l Conf.
on Big Data. Los Angeles: IEEE, 2019. 2552−2559.
[16] Nadiger C, Kumar A, Abdelhak S. Federated reinforcement learning for fast personalization. In: Proc. of the 2019 IEEE 2nd Int’l
Conf. on Artificial Intelligence and Knowledge Engineering (AIKE). Sardinia: IEEE, 2019. 123−127.
[17] Li Q, Wen Z, He B. Practical federated gradient boosting decision trees. In: Proc. of the 34th AAAI Conf. on Artificial Intelligence
(AAAI 2020). New York: AAAI, 2020. 4642−4649.
[18] Yurochkin M, Agarwal M, Ghosh S, et al. Bayesian nonparametric federated learning of neural networks. In: Proc. of the Int’l Conf.
on Machine Learning. Long Beach: PMLR, 2019. 7252−7261.
[19] Liu Y, Kang Y, Xing C, et al. A secure federated transfer learning framework. IEEE Intelligent Systems. 2020,35(4):70−82. [doi:
10.1109/MIS.2020.2988525]
[20] Nadiger C, Kumar A, Abdelhak S. Federated reinforcement learning for fast personalization. In: Proc. of the IEEE 2nd Int’l Conf.
on Artificial Intelligence and Knowledge Engineering (AIKE). Sardinia: IEEE, 2019. 123−127.
[21] Ke G, Meng Q, Finley T, et al. Lightgbm: A highly efficient gradient boosting decision tree. In: Advances in Neural Information
Processing Systems. Hangzhou: IEEE, 2017. 3146−3154.
[22] Sharma S, Chen K. Privacy-preserving boosting with random linear classifiers. In: Proc. of the 2018 ACM SIGSAC Conf. on
Computer and Communications Security. Toronto: ACM, 2018. 2294−2296.