Page 285 - 《软件学报》2026年第6期
P. 285
2604 软件学报 2026 年第 37 卷第 6 期
127663. [doi: 10.1016/j.neucom.2024.127663]
[4] Narayanan A, Shmatikov V. Robust de-anonymization of large sparse datasets. In: Proc. of the 2008 IEEE Symp. on Security and
Privacy. Oakland: IEEE, 2008. 111–125. [doi: 10.1109/SP.2008.33]
[5] Yang L, Tan B, Zheng VW, Chen K, Yang Q. Federated recommendation systems. In: Yang Q, Fan LX, Yu H, eds. Federated Learning:
Privacy and Incentive. Cham: Springer, 2020. 225–239. [doi: 10.1007/978-3-030-63076-8_16]
[6] Guo L, Lu ZA, Yu JL, Nguyen QVH, Yin HZ. Prompt-enhanced federated content representation learning for cross-domain
recommendation. In: Proc. of the 2024 ACM Web Conf. Singapore: ACM, 2024. 3139–3149. [doi: 10.1145/3589334.3645337]
[7] Lin ZH, Huang W, Zhang HY, Xu JY, Liu WM, Liao XT, Wang F, Wang SP, Tan YC. Enhancing dual-target cross-domain
recommendation with federated privacy-preserving learning. In: Proc. of the 33rd Int’l Joint Conf. on Artificial Intelligence. Jeju, 2024.
238. [doi: 10.24963/ijcai.2024/238]
[8] Muhammad K, Wang QQ, O’Reilly-Morgan D, Tragos E, Smyth B, Hurley N, Geraci J, Lawlor A. FedFast: Going beyond average for
faster training of federated recommender systems. In: Proc. of the 26th ACM SIGKDD Int’l Conf. on Knowledge Discovery & Data
Mining. ACM, 2020. 1234–1242. [doi: 10.1145/3394486.3403176]
[9] McMahan B, Moore E, Ramage D, Hampson S, Agüera y Arcas B. 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.
[10] Hamer J, Mohri M, Suresh A T. FedBoost: A communication-efficient algorithm for federated learning. In: Proc. of the 37th Int’l Conf.
on Machine Learning. PMLR, 2020. 3973–3983.
[11] Niu CY, Wu F, Tang SJ, Hua LF, Jia RF, Lv CF, Wu ZH, Chen GH. Billion-scale federated learning on mobile clients: A submodel
design with tunable privacy. In: Proc. of the 26th Annual Int’l Conf. on Mobile Computing and Networking. London: ACM, 2020. 31.
[doi: 10.1145/3372224.3419188]
[12] Chai D, Wang LY, Chen K, Yang Q. Secure federated matrix factorization. IEEE Intelligent Systems, 2021, 36(5): 11–20. [doi: 10.1109/
MIS.2020.3014880]
[13] Song CZ, Ristenpart T, Shmatikov V. Machine learning models that remember too much. In: Proc. of the 2017 ACM SIGSAC Conf. on
Computer and Communications Security. Dallas: ACM, 2017. 587–601. [doi: 10.1145/3133956.3134077]
[14] Zhang SJ, Yuan W, Yin HZ. Comprehensive privacy analysis on federated recommender system against attribute inference attacks. IEEE
Trans. on Knowledge and Data Engineering, 2024, 36(3): 987–999. [doi: 10.1109/TKDE.2023.3295601]
[15] Truex S, Liu L, Chow KH, Gursoy ME, Wei WQ. LDP-Fed: Federated learning with local differential privacy. In: Proc. of the 3rd ACM
Int’l Workshop on Edge Systems, Analytics and Networking. Heraklion: ACM, 2020. 61–66. [doi: 10.1145/3378679.3394533]
[16] Buyukates B, Ulukus S. Timely communication in federated learning. In: Proc. of the 2021 IEEE Conf. on Computer Communications
Workshops (INFOCOM WKSHPS). Vancouver: IEEE, 2021: 1–6. [doi: 10.1109/INFOCOMWKSHPS51825.2021.9484497]
[17] Li ZT, Ding BL, Zhang C, Li N, Zhou J. Federated matrix factorization with privacy guarantee. Proc. of the VLDB Endowment, 2021,
15(4): 900–913. [doi: 10.14778/3503585.3503598]
[18] Rong DZ, He QM, Chen JH. Poisoning deep learning based recommender model in federated learning scenarios. In: Proc. of the 31st Int’l
Joint Conf. on Artificial Intelligence (IJCAI 2022). IJCAI, 2022. 2204–2210.
[19] Wang QY, Yin HZ, Chen T, Yu JL, Zhou A, Zhang XL. Fast-adapting and privacy-preserving federated recommender system. The
VLDB Journal, 2022, 31(5): 877–896. [doi: 10.1007/s00778-021-00700-6]
[20] Fung BCM, Wang K, Chen R, Yu PS. Privacy-preserving data publishing: A survey of recent developments. ACM Computing Surveys
(CSUR), 2010, 42(4): 14. [doi: 10.1145/1749603.1749605]
[21] Salakhutdinov R, Mnih A. Probabilistic matrix factorization. In: Proc. of the 21st Int’l Conf. on Neural Information Processing Systems.
Vancouver: Curran Associates Inc., 2007. 1257–1264.
[22] Yin HZ, Wang QY, Zheng K, Li ZX, Yang JL, Zhou XF. Social influence-based group representation learning for group
recommendation. In: Proc. of the 35th IEEE Int’l Conf. on Data Engineering (ICDE). Macao: IEEE, 2019: 566–577. [doi: 10.1109/ICDE.
2019.00057]
[23] Perifanis V, Efraimidis PS. Federated neural collaborative filtering. Knowledge-based Systems, 2022, 242: 108441. [doi: 10.1016/j.
knosys.2022.108441]
[24] Lin GY, Liang F, Pan WK, Ming Z. FedRec: Federated recommendation with explicit feedback. IEEE Intelligent Systems, 2021, 36(5):
21–30. [doi: 10.1109/MIS.2020.3017205]
[25] Duan SJ, Zhang DY, Wang YB, Li LX, Zhang YX. JointRec: A deep-learning-based joint cloud video recommendation framework for
mobile IoT. IEEE Internet of Things Journal, 2020, 7(3): 1655–1666. [doi: 10.1109/JIOT.2019.2944889]
[26] Huang W, Liu J, Li TR, Huang TQ, Ji SG, Wan JH. FedDSR: Daily schedule recommendation in a federated deep reinforcement learning

