Personalized Manufacturing Service Recommendation Method Based on Federated Learning Framework

doi:10.3901/JME.2023.12.149

Abstract

Abstract: To solve the problem of the massive manufacturing services in the industrial cloud platform make it difficult for users to select services and the use of recommendation algorithm makes the platform have the risk of divulging user data, a personalized recommendation method of manufacturing services based on Federated Learning framework is proposed. The model-based Collaborative Filtering recommendation algorithm is adopted, and the scoring bias of users and services is considered in the matrix decomposition to improve the recommendation accuracy. Data used in the recommendation algorithm is classified and stored in the client and server, of which data containing users' privacy is retained locally. Local data is combined with data in the server to train the model after being desensitized by the encryption algorithm. Finally, users' scores on different attributes in manufacturing service are predicted. Users’ personalization tendency is further quantified by multi-attribute prediction scores according to the characteristics of manufacturing service quality. Pareto non-dominant solution set of the service is obtained through the intervention of the personalization tendency factor on the prediction score, and the recommended list of the manufacturing service is generated. The experiment shows that the recommendation method can obtain good prediction accuracy without compromising users’ privacy and the average prediction score of the recommendation service is high with users’ personalized preferences being considered, thus helping users better choose manufacturing services.

Key words: manufacturing service recommendation, federated learning, collaborative filtering, quality of service, multi-attribute rating

CLC Number:

TP391

WANG Lei, JIN Xiao, TANG Hongtao, LI Xixing, LI Yibing, GUO Shunsheng, GUAN Sijia. Personalized Manufacturing Service Recommendation Method Based on Federated Learning Framework[J]. Journal of Mechanical Engineering, 2023, 59(12): 149-161.

References

[1] 李伯虎,张霖,王时龙,等. 云制造——面向服务的网络化制造新模式[J]. 计算机集成制造系统,2010,16(1):8. LI Bohu,ZHANG Lin,WANG Shilong,et al. Cloud manufacturing:A new service-oriented networked manufacturing model[J]. Computer Integrated Manufacturing System,2010,16(1):8.
[2] 陶飞,戚庆林. 面向服务的智能制造[J]. 机械工程学报,2018,54(16):11-23. TAO Fei,QI Qinglin. Service-oriented smart manufacturing[J]. Journal of Mechanical Engineering,2018,54(16):11-23.
[3] 郝予实,范玉顺. 基于场景识别的云制造服务推荐[J]. 计算机集成制造系统,2020,26(8):13. HAO Yushi,FAN Yushun. Cloud manufacturing service recommendation based on scenario recognition[J]. Computer Integrated Manufacturing System,2020,26(8):13.
[4] 方辉,谭建荣,谭颖,等. 基于Web的制造信息主动推荐服务研究[J]. 计算机集成制造系统,2008,14(11):2253-2260. FANG Hui,TAN Jianrong,TAN Ying,et al. Manufacturing information active recommendation based on Web services[J]. Computer Integrated Manufacturing System,2008,14(11):2253-2260.
[5] ADOMAVICIUS G,TUZHILIN A. Toward the next generation of recommender systems:A survey of the state-of-the-art and possible extensions[J]. IEEE Transactions on Knowledge and Data Engineering,2005,17(6):734-749.
[6] CAO Y,WANG S,KANG L,et al. Study on machining service modes and resource selection strategies in cloud manufacturing[J]. International Journal of Advanced Manufacturing Technology,2015,81(1-4):597-613.
[7] 冯长利,马睿泽. 基于服务化的制造企业与服务提供商的演化博弈分析[J]. 中国管理科学,2022,30(6):263-274. FENG Changli,MA Ruize. Analysis of evolutionary game between manufacturing enterprises and service providers based on servitization[J]. Chinese Journal of Management Science,2022,30(6):263-274.
[8] 胡业发,陶飞,周祖德. 制造网格资源服务Trust-QoS评估及其应用[J]. 机械工程学报,2007,43(12):203-211. HU Yefa,TAO Fei,ZHOU Zude. Manufacturing grid resource service trust-qos evaluation and application[J]. Journal of Mechanical Engineering,2007,43(12):203-211.
[9] 张映锋,郭振刚,钱成,等. 基于过程感知的底层制造资源智能化建模及其自适应协同优化方法研究[J]. 机械工程学报,2018,54(16):1-10. ZHANG Yingfeng,GUO Zhengang,QIAN Cheng,et al. Investigation on process-aware based intelligent modeling of bottom layer manufacturing resources and self-adaptive collaborative optimization methodology[J]. Journal of Mechanical Engineering,2018,54(16):1-10.
[10] 许海玲,吴潇,李晓东,等. 互联网推荐系统比较研究[J].软件学报,2009,20(2):350-362. XU Hailing,WU Xiao,LI Xiaodong,et al. Comparison study of internet recommendation system.[J]. Journal of Software,2009,20(2):350-362.
[11] YANG Q,LIU Y,CHEN T,et al. Federated machine learning:Concept and applications[J]. ACM Transactions on Intelligent Systems and Technology,2019,10(2):1-19.
[12] 孙光浩,刘丹青,李梦云. 个性化推荐算法综述[J]. 软件,2017,38(7):70-78. SUN GuangHao,LIU Danqing,LI Mengyun. A survey of personalized recommendation algorithms[J]. Software,2017,38(7):70-78.
[13] LATHA R. Enhancing recommendation competence in nearest neighbour models[J]. Physica a-Statistical Mechanics and Its Applications,2022,59,592.
[14] KANT S,MAHARA T. Nearest biclusters collaborative filtering framework with fusion[J]. Journal of Computational Science,2018,25:204-212.
[15] SENG D,CHEN G,ZHANG Q. Item-based collaborative memory networks for recommendation[J]. IEEE Access,2020(8):213027-213037.
[16] KIM D,PARK C,OH J,et al. Deep hybrid recommender systems via exploiting document context and statistics of items[J]. Information Sciences,2017,417:72-87.
[17] LI M,SHENG L,SONG Y,et al. An enhanced matrix completion method based on non-negative latent factors for recommendation system[J]. Expert Systems with Applications,2022,201:116985.
[18] WANG R,CHENG H K,JIANG Y,et al. A novel matrix factorization model for recommendation with LOD-based semantic similarity measure[J]. Expert Systems with Applications,2019,123:70-81.
[19] SATTLER F,WIEDEMANN S,MUELLER K R,et al. Robust and communication-efficient federated learning from non-i.i.d. data[J]. IEEE Transactions on Neural Networks and Learning Systems,2020,31(9):3400-3413.
[20] JIAO Y,WANG P,NIYATO D,et al. Toward an automated auction framework for wireless federated learning services market[J]. IEEE Transactions on Mobile Computing,2021,20(10):3034-3048.
[21] KHAN L U,PANDEY S R,TRAN N H,et al. Federated learning for edge networks:Resource optimization and incentive mechanism[J]. IEEE Communications Magazine,2020,58(10):88-93.
[22] MILLS J,HU J,MIN G. Communication-efficient federated learning for wireless edge intelligence in IoT[J]. IEEE Internet of Things Journal,2020,7(7):5986-5994.
[23] KAISSIS G,ZILLER A,PASSERAT-PALMBACH J,et al. End-to-end privacy preserving deep learning on multi-institutional medical imaging[J]. Nature Machine Intelligence,2021,3(6):473-484.
[24] 鲜征征,李启良,黄晓宇,等. 基于差分隐私和SVD++的协同过滤算法[J].控制与决策,2019,34(1):43-54. XIAN Zhengzheng,LI Qiliang,HUANG Xiaoyu,et al. Collaborative filtering via SVD++ with differential privacy[J]. Control and Decision,2019,34(1):43-54.
[25] CHAI D,WANG L,CHEN K,et al. Secure federated matrix factorization[J]. Intelligent Systems,IEEE,2021,36(5):11-19.
[26] WANG Y,TIAN Y,YIN X,et al. A trusted recommendation scheme for privacy protection based on federated learning[J]. CCF Transactions on Networking,2020,3(3-4):218-228.
[27] ZHOU P,WANG K,GUO L,et al. A privacy-preserving distributed contextual federated online learning framework with big data support in social recommender systems[J]. IEEE Transactions on Knowledge and Data Engineering,2021,33(3):824-838.
[28] FENG Y,HUANG B. Cloud manufacturing service QoS prediction based on neighbourhood enhanced matrix factorization[J]. Journal of Intelligent Manufacturing,2020,31(7):1649-1660.
[29] FENG Y,HUANG B. A hierarchical and configurable reputation evaluation model for cloud manufacturing services based on collaborative filtering[J]. The International Journal of Advanced Manufacturing Technology,2018,94(9):3327-3343.
[30] ZHANG S,YANG W,XU S,et al. A hybrid social network-based collaborative filtering method for personalized manufacturing service recommendation[J]. International Journal of Computers,Communications & Control (IJCCC),2017,12(5):728.
[31] LIU J,CHEN Y. A personalized clustering-based and reliable trust-aware QoS prediction approach for cloud service recommendation in cloud manufacturing[J]. Knowledge-Based Systems,2019,174(15):43-56.
[32] ZHANG W,ZHANG S,CHEN Y G,et al. Combining social network and collaborative filtering for personalised manufacturing service recommendation[J]. International Journal of Production Research,2013,51(22):6702-6719.
[33] ZHANG W,GUO S,ZHANG S. Personalized manufacturing service recommendation using semantics-based collaborative filtering[J]. Concurrent Engineering,2015,23(2):166-179.
[34] ZHANG W,ZHANG S,ZHANG S,et al. A novel method for MCDM and evaluation of manufacturing services using collaborative filtering and IVIF theory[J]. Journal of Algorithms and Computational Technology,2016,10(1):40-51.
[35] 马文龙,朱李楠,王万良. 云制造环境下基于QoS感知的云服务选择模型[J]. 计算机集成制造系统,2014,20(5):1246-1254. MA Wenlong,ZHU Linan,WANG Wanliang. Cloud service selection model based on QoS-aware in cloud manufacturing environment[J]. Computer Integrated Manufacturing System,2014,20(5):1246-1254.
[36] 赵道致,李锐. 考虑主体心理预期的云制造资源双边匹配机制[J]. 控制与决策,2017,32(5):871-878. LI Daozhi,LI Rui. Two-sided matching mechanism with agents'expectation for cloudmanufacturing resource[J]. Control and Decision,2017,32(5):871-878.
[37] 任磊,任明仑. 基于加权协同网络的制造服务组合方法[J]. 机械工程学报,2018,54(16):70-78. REN Lei,REN Minglun. Manufacturing service composition method based on service weighted synergy network[J]. Journal of Mechanical Engineering,2018,54(16):70-78.
[38] 任磊,任明仑. 基于情景感知的制造组合服务自适应决策机制[J]. 控制与决策,2019,34(6):1277-1285. REN Lei,REN Minglun. Situation aware-adaptive decision-making mechanism of manufacturing composition service[J]. Control and Decision,2019,34(6):1277-1285.
[39] LI H,WEI W,FAN R. Deep Learning-based QoS prediction for manufacturing cloud service[C]//Proceedings of the 38th Chinese Control Conference (CCC). Guangzhou:IEEE,2019:2719-2724.
[40] LIU Z,WANG L,LI X,et al. A multi-attribute personalized recommendation method for manufacturing service composition with combining collaborative filtering and genetic algorithm[J]. Journal of Manufacturing Systems,2021,58:348-364.
[41] ZHAO S,ZHANG Q,PENG Z,et al. Personalized manufacturing service composition recommendation:Combining combinatorial optimization and collaborative filtering[J]. Journal of Combinatorial Optimization,2020,40(3):733-756.
[42] ZHENG H,FENG Y,TAN J. A fuzzy QoS-aware resource service selection considering design preference in cloud manufacturing system[J]. The International Journal of Advanced Manufacturing Technology,2016,84(1-4):371-379.
[43] CHEN F,DOU R,LI M,et al. A flexible QoS-aware Web service composition method by multi-objective optimization in cloud manufacturing[J]. Computers & Industrial Engineering,2016,99:423-431.
[44] KOREN Y,BELL R,VOLONSKY C. Matrix factorization techniques for recommender systems[J]. Computer,2009,42(8):30-37.
[45] PHONG L,AONO Y,HAYASHI T,et al. Privacy-preserving deep learning via additively homomorphic encryption[J]. IEEE Transactions on Information Forensics and Security,2018,13(5):1333-1345.