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IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2025  > In Press, Accepted Manuscript
X. Jia, J. Li, S. Wang, H. Qi, F.-Y. Wang, R. Qin, M. Zhang, and X. Liang, “Federated services: A smart service ecology with federated security for aligned data supply and scenario-oriented demands,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 5, pp. 1–12, May 2025.
Citation: X. Jia, J. Li, S. Wang, H. Qi, F.-Y. Wang, R. Qin, M. Zhang, and X. Liang, “Federated services: A smart service ecology with federated security for aligned data supply and scenario-oriented demands,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 5, pp. 1–12, May 2025.
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Federated Services: A Smart Service Ecology With Federated Security for Aligned Data Supply and Scenario-Oriented Demands

Funds:  This work was partially supported by the National Key Research and Development Program of China (2021YFB2104800), the National Natural Science Foundation of China (62103411), and the Science and Technology Development Fund of Macau SAR (0093/2023/RIA2, 0050/2020/A1)
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    Abstract
  • This paper introduces federated services as a smart service ecology with federated security to align distributed data supply with diversified service demands spanning digital and societal contexts. It presents the comprehensive researches on the theoretical foundation and technical system of federated services, aiming at advancing our understanding and implementation of this novel service paradigm. First, a thorough examination of the characteristics of federated security within federated services is conducted. Then, a five-layer technical framework is formulated under a decentralized intelligent architecture, ensuring secure, agile, and adaptable service provision. On this basis, the operational mechanisms underlying data confedration and service confederation is analyzed, with emphasis on the smart supply-demand matching model. Furthermore, a scenario-oriented taxonomy of federated services accompanied by illustrative examples is proposed. Our work offers actionable insights and roadmap for realizing and advancing federated services, contributing to the refinement and wider adoption of this transformative service paradigm in the digital era.

     

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  • [1]
    X. Jia, S. Gao, Y. Zhou, Q. Xue, and J. Fan, “A technical framework of efficient cross-domain data circulation for mega-city governance,” Front. Data Comput., vol. 5, no. 5, pp. 35–45, Oct. 2023.
    [2]
    M. Papa, I. Chatzigiannakis, and A. Anagnostopoulos, “Automated natural language processing-based supplier discovery for financial services,” Big Data, vol. 12, no. 1, pp. 30–48, Feb. 2024. doi: 10.1089/big.2022.0215
    [3]
    H. Herjanto, M. Amin, and E. F. Purington, “Panic buying: The effect of thinking style and situational ambiguity,” J. Retail. Consum. Serv., vol. 60, p. 102455, May 2021. doi: 10.1016/j.jretconser.2021.102455
    [4]
    S. Akter, S. Motamarri, U. Hani, R. Shams, M. Fernando, M. M. Babu, and K. N. Shen, “Building dynamic service analytics capabilities for the digital marketplace,” J. Bus. Res., vol. 118, pp. 177–188, Sept. 2020. doi: 10.1016/j.jbusres.2020.06.016
    [5]
    X. Jia. S. Gao, X. Jiang, H. Qi, X. Wang, J. Zhang, R. Qin, and L. Ouyang, “Research on key technologies of social computing for urban complex system,” Chin. J. Intell. Sci. Technol., vol. 3, no. 2, pp. 228–233, Jun. 2021.
    [6]
    H. Kaur, H. Nori, S. Jenkins, R. Caruana, H. Wallach, and J. W. Vaughan, “Interpreting interpretability: Understanding data scientists' use of interpretability tools for machine learning,” in Proc. CHI Conf. Human Factors in Computing Systems, Honolulu, USA, 2020, pp. 1–14.
    [7]
    J. W. Peltier, A. J. Dahl, and E. L. Swan, “Digital information flows across a B2C/C2C continuum and technological innovations in service ecosystems: A service-dominant logic perspective,” J. Bus. Res., vol. 121, pp. 724–734, Dec. 2020. doi: 10.1016/j.jbusres.2020.03.020
    [8]
    A. Dosis and W. Sand-Zantman, “The ownership of data,” J. Law, Econ. Organ., vol. 39, no. 3, pp. 615–641, Nov. 2023. doi: 10.1093/jleo/ewac001
    [9]
    X. Li, H. Zhao, and W. Deng, “BFOD: Blockchain-based privacy protection and security sharing scheme of flight operation data,” IEEE Internet Things J., vol. 11, no. 2, pp. 3392–3401, Jan. 2024. doi: 10.1109/JIOT.2023.3296460
    [10]
    P. Singh, M. Masud, M. Shamim Hossain, and A. Kaur, “Cross-domain secure data sharing using blockchain for industrial IoT,” J. Parallel Distrib. Comput., vol. 156, pp. 176–184, Oct. 2021. doi: 10.1016/j.jpdc.2021.05.007
    [11]
    O. H. Chi, C. G. Chi, D. Gursoy, and R. Nunkoo, “Customers' acceptance of artificially intelligent service robots: The influence of trust and culture,” Int. J. Inf. Manage., vol. 70, p. 102623, Jun. 2023. doi: 10.1016/j.ijinfomgt.2023.102623
    [12]
    A. Falk, A. Becker, T. Dohmen, D. Huffman, and U. Sunde, “The preference survey module: A validated instrument for measuring risk, time, and social preferences,” Manage. Sci., vol. 69, no. 4, pp. 1935–1950, Apr. 2023.
    [13]
    A. Wang, L. Lei, E. Lagunas, A. I. Pérez-Neira, S. Chatzinotas, and B. Ottersten, “NOMA-enabled multi-beam satellite systems: Joint optimization to overcome offered-requested data mismatches,” IEEE Trans. Veh. Technol., vol. 70, no. 1, pp. 900–913, Jan. 2021.
    [14]
    M. M. Pereira and E. Frazzon, “A data-driven approach to adaptive synchronization of demand and supply in omni-channel retail supply chains,” Int. J. Inf. Manage., vol. 57, p. 102165, Apr. 2021.
    [15]
    M. Naeem, T. Jamal, J. Diaz-Martinez, S. A. Butt, N. Montesano, M. I. Tariq, E. De-la-Hoz-Franco, and E. De-La-Hoz-Valdiris, “Trends and future perspective challenges in big data,” in Proc. 6th Euro-China Conf. Intelligent Data Analysis and Applications, Arad, Romania, 2019, pp. 309–325.
    [16]
    K. Hopf, A. Weigert, and T. Staake, “Value creation from analytics with limited data: A case study on the retailing of durable consumer goods,” J. Decis. Syst., vol. 32, no. 2, pp. 289–325, Apr. 2023.
    [17]
    J. L. Monino, “Data value, big data analytics, and decision-making,” J. Knowl. Econ., vol. 12, no. 1, pp. 256–267, Mar. 2021.
    [18]
    F.-Y. Wang, Y. Wang, Y. Chen, Y. Tian, H. Qi, X. Wang, W. Zhang, J. Zhang, and Y. Yuan, “Federated ecology: From federated data to federated intelligence,” Chin. J. Intell. Sci. Technol., vol. 2, no. 4, pp. 305–311, Dec. 2020.
    [19]
    C. Zhao, X. Dai, Y. Lv, J. Niu, and Y. Lin, “Decentralized autonomous operations and organizations in TransVerse: Federated intelligence for smart mobility,” IEEE Trans. Syst. Man Cybern. Syst., vol. 53, no. 4, pp. 2062–2072, Apr. 2023.
    [20]
    X. Jia, R. Qin, S. Wang, H. Qi, F.-Y. Wang, J. Li, M. Zhang, and X. Liang, “Federated services: Smart service paradigm based on distributed data co-governance,” Chin. J. Intell. Sci. Technol., vol. 6, no. 2, pp. 210–219, Jun. 2024.
    [21]
    F.-Y. Wang, W. Zhang, Y. Tian, R. Qin, X. Wang, and B. Hu, “Federated data: Toward new generation of credible and trustable artificial intelligence,” IEEE Trans. Comput. Soc. Syst., vol. 8, no. 3, pp. 538–545, Jun. 2021.
    [22]
    F.-Y. Wang, “New control paradigm for industry 5.0: From big models to foundation control and management,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 8, pp. 1643–1646, Aug. 2023.
    [23]
    F.-Y. Wang, J. Yang, X. Wang, J. Li, and Q.-L. Han, “Chat with ChatGPT on industry 5.0: Learning and decision-making for intelligent industries,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 4, pp. 831–834, Apr. 2023. doi: 10.1109/JAS.2023.123552
    [24]
    S. Wang, L. Ouyang, Y. Yuan, X. Ni, X. Han, F.-Y. Wang, “Blockchain-enabled smart contracts: Architecture, applications, and future trends,” IEEE Trans. Syst. Man Cybern. Syst., vol. 49, no. 11, pp. 2266–2277, Nov. 2019.
    [25]
    F.-Y. Wang, R. Qin, J. Li, X. Wang, H. Qi, X. Jia, and B. Hu, “Federated management: Toward federated services and federated security in federated ecology,” IEEE Trans. Comput. Soc. Syst., vol. 8, no. 6, pp. 1283–1290, Dec. 2021.
    [26]
    J. Li, S. Guan, R. Qin, J. Hou, and F.-Y. Wang, “Intelligent blockchains and blockchain intelligence: The infrastructure intelligence for DePIN,” Chin. J. Intell. Sci. Technol., vol. 6, no. 1, pp. 5–16, Mar. 2024.
    [27]
    R. Qin, W. Ding, J. Li, S. Guan, G. Wang, and Y. Ren, “Web3-based decentralized autonomous organizations and operations: Architectures, models, and mechanisms,” IEEE Trans. Syst. Man Cybern. Syst., vol. 53, no. 4, pp. 2073–2082, Apr. 2023. doi: 10.1109/TSMC.2022.3228530
    [28]
    J. Li and F.-Y. Wang, “The TAO of blockchain intelligence for intelligent Web 3.0,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 12, pp. 2183–2186, Dec. 2023. doi: 10.1109/JAS.2023.124056
    [29]
    F.-Y. Wang, K. Kyriakopoulos, A. Tsolkas, and G. N. Saridis, “A Petri-net coordination model for an intelligent mobile robot,” IEEE Trans. Syst. Man Cybern., vol. 21, no. 4, pp. 777–789, Jul.–Aug. 1991. doi: 10.1109/21.108296
    [30]
    X. Li, P. Ye, J. Lim, Z. Liu, L. Cao, and F.-Y. Wang, “From features engineering to scenarios engineering for trustworthy AI: I&I, C&C, and V&V,” IEEE Intell. Syst., vol. 37, no. 4, pp. 18–26, Jul.-Aug. 2022. doi: 10.1109/MIS.2022.3197950
    [31]
    X. Li, R. Song, J. Fan, M. Liu, and F.-Y. Wang, “Development and testing of advanced driver assistance systems through scenario-based systems engineering,” IEEE Trans. Intell. Veh., vol. 8, no. 8, pp. 3968–3973, Aug. 2023. doi: 10.1109/TIV.2023.3297168
    [32]
    Y. Yuan and F.-Y. Wang, “Blockchain: The state of the art and future trends,” Acta Autom. Sinica, vol. 42, no. 4, pp. 481–494, Apr. 2016.
    [33]
    J. Li, J. Li, X. Wang, R. Qin, Y. Yuan, and F.-Y. Wang, “Multi-blockchain based data trading markets with novel pricing mechanisms,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 12, pp. 2222–2232, Dec. 2023. doi: 10.1109/JAS.2023.123963
    [34]
    F. Fioretto and P. Van Hentenryck, “Privacy-preserving federated data sharing,” in Proc. 18th Int. Conf. Autonomous Agents and MultiAgent Systems, Montreal, Canada, 2019, pp. 638–646.
    [35]
    P. V. Astillo, D. Duguma, H. Park, J. Kim, B. Kim, and I. You, “Federated intelligence of anomaly detection agent in IoTMD-enabled Diabetes Management Control System,” Future Gener. Comput. Syst., vol. 128, pp. 395–405, Mar. 2022. doi: 10.1016/j.future.2021.10.023
    [36]
    H. Chen, F. W. Wang, and D. Zeng, “Intelligence and security informatics for homeland security: Information, communication, and transportation,” IEEE Trans. Intell. Transp. Syst., vol. 5, no. 4, pp. 329–341, Dec. 2004. doi: 10.1109/TITS.2004.837824
    [37]
    S. Wang, W. Ding, J. Li, Y. Yuan, L. Ouyang, and F.-Y. Wang, “Decentralized autonomous organizations: Concept, model, and applications,” IEEE Trans. Comput. Soc. Syst., vol. 6, no. 5, pp. 870–878, Oct. 2019. doi: 10.1109/TCSS.2019.2938190
    [38]
    J. Li, R. Qin, S. Guan, W. Ding, F. Lin, and F.-Y. Wang, “Attention markets of blockchain-based decentralized autonomous organizations,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 6, pp. 1370–1380, Jun. 2024. doi: 10.1109/JAS.2024.124491
    [39]
    F.-Y. Wang, “Parallel intelligence in metaverses: Welcome to hanoi!” IEEE Intell. Syst., vol. 37, no. 1, pp. 16–20, Jan.–Feb. 2022. doi: 10.1109/MIS.2022.3154541
    [40]
    F.-Y. Wang, Q. Miao, X. Li, X. Wang, and Y. Lin, “What does ChatGPT say: The DAO from algorithmic intelligence to linguistic intelligence,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 3, pp. 575–579, Mar. 2023. doi: 10.1109/JAS.2023.123486
    [41]
    K. Wu, Y. Ma, G. Huang, and X. Liu, “A first look at blockchain-based decentralized applications,” Softw. Pract. Exp., vol. 51, no. 10, pp. 2033–2050, Oct. 2021. doi: 10.1002/spe.2751
    [42]
    N. Fei, Z. Lu, Y. Gao, G. Yang, Y. Huo, J. Wen, H. Lu, R. Song, X. Gao, T. Xiang, H. Sun, and J. R. Wen, “Towards artificial general intelligence via a multimodal foundation model,” Nat. Commun., vol. 13, p. 3094, Jun. 2022. doi: 10.1038/s41467-022-30761-2
    [43]
    J. Li, R. Qin, and F.-Y. Wang, “The future of management: DAO to smart organizations and intelligent operations,” IEEE Trans. Syst. Man, Cybern. Syst., vol. 53, no. 6, pp. 3389–3399, Jun. 2023. doi: 10.1109/TSMC.2022.3226748
    [44]
    F.-Y. Wang, H. Zhang, and D. Liu, “Adaptive dynamic programming: An introduction,” IEEE Comput. Intell. Mag., vol. 4, no. 2, pp. 39–47, May 2009. doi: 10.1109/MCI.2009.932261
    [45]
    N. Kshetri, “A typology of metaverses,” Computer, vol. 55, no. 12, pp. 150–155, Dec. 2022. doi: 10.1109/MC.2022.3204978
    [46]
    J. Lu, X. Wang, X. Cheng, J. Yang, O. Kwan, and X. Wang, “Parallel factories for smart industrial operations: From big AI models to field foundational models and scenarios engineering,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 12, pp. 2079–2086, Dec. 2022. doi: 10.1109/JAS.2022.106094
    [47]
    J. Li, R. Qin, S. Guan, J. Hou, and F.-Y. Wang, “Blockchain intelligence: Intelligent blockchains for web 3.0 and beyond,” IEEE Trans. Syst. Man Cybern. Syst., vol. 54, no. 11, pp. 6633–6642, Nov. 2024. doi: 10.1109/TSMC.2023.3348449
    [48]
    J. Wu, W. Gan, Z. Chen, S. Wan, and P. S. Yu, “Multimodal large language models: A survey,” in Proc. IEEE Int. Conf. Big Data, Sorrento, Italy, 2023, pp. 2247–2256.
    [49]
    Y. Wang, M. Kang, Y. Liu, J. Li, K. Xue, X. Wang, J. Du, Y. Tian, Qi. Ni, and F.-Y. Wang, “Can digital intelligence and cyber-physical-social systems achieve global food security and sustainability?” IEEE/CAA J. Autom. Sinica, vol. 10, no. 11, pp. 2070–2080, Nov. 2023. doi: 10.1109/JAS.2023.123951
    [50]
    T. Shen, J. Sun, S. Kong, Y. Wang, J. Li, X. Li, and F.-Y. Wang, “The journey/DAO/TAO of embodied intelligence: From large models to foundation intelligence and parallel intelligence,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 6, pp. 1313–1316, Jun. 2024. doi: 10.1109/JAS.2024.124407
    [51]
    Y. Tian, J. Wang, Y. Wang, C. Zhao, F. Yao, and X. Wang, “Federated vehicular transformers and their federations: Privacy-preserving computing and cooperation for autonomous driving,” IEEE Trans. Intell. Veh., vol. 7, no. 3, pp. 456–465, Sept. 2022. doi: 10.1109/TIV.2022.3197815
    [52]
    Z. Chen and N. Li, “An optimal control-based distributed reinforcement learning framework for a class of non-convex objective functionals of the multi-agent network,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 11, pp. 2081–2093, Nov. 2023. doi: 10.1109/JAS.2022.105992
    [53]
    A. Joshi, S. Capezza, A. Alhaji, and M. Y. Chow, “Survey on AI and machine learning techniques for microgrid energy management systems,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 7, pp. 1513–1529, Jul. 2023. doi: 10.1109/JAS.2023.123657
    [54]
    J. Zhang, “Knowledge learning with crowdsourcing: A brief review and systematic perspective,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 5, pp. 749–762, May 2022. doi: 10.1109/JAS.2022.105434
    [55]
    J. Li, M. Zhou, X. Xue, J. Zhu, S. Han, L. Li, and F.-Y. Wang, “A sustainable ecology of mobility: DAO-based autonomous vehicular services,” IEEE Trans. Intell. Veh., vol. 8, no. 12, pp. 4682–4684, Dec. 2023. doi: 10.1109/TIV.2023.3334719
    [56]
    X. Dai, M. Vallati, R. Guo, Y. Wang, S. Han, and Y. Lin, “THE ROAD AHEad: DAO-secured V2X infrastructures for safe and smart vehicular management,” IEEE Trans. Intell. Veh., vol. 8, no. 12, pp. 4674–4677, Dec. 2023. doi: 10.1109/TIV.2023.3337993
    [57]
    M. Qi, Z. Wang, Q.-L. Han, J. Zhang, S. Chen, and Y. Xiang, “Privacy protection for blockchain-based healthcare IoT systems: A survey,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 8, pp. 1757–1776, Aug. 2024. doi: 10.1109/JAS.2022.106058
    [58]
    P. Lang, D. Tian, X. Duan, J. Zhou, Z. Sheng, V. C. M. Leung, “Cooperative computation offloading in blockchain-based vehicular edge computing networks,” IEEE Trans. Intell. Veh., vol. 7, no. 3, pp. 783–798, Sept. 2022. doi: 10.1109/TIV.2022.3190308
    [59]
    A. Chaddad, Q. Lu, J. Li, Y. Katib, R. Kateb, C. Tanougast, A. Bouridane, and A. Abdulkadir, “Explainable, domain-adaptive, and federated artificial intelligence in medicine,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 4, pp. 859–876, Apr. 2023. doi: 10.1109/JAS.2023.123123
    [60]
    X. Jiang, X. Kong, and Z. Ge, “Augmented industrial data-driven modeling under the curse of dimensionality,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 6, pp. 1445–1461, Jun. 2023. doi: 10.1109/JAS.2023.123396
    [61]
    U. Lee, G. Jung, E. Y. Ma, J. S. Kim, H. Kim, J. Alikhanov, Y. Noh, and H. Kim, “Toward data-driven digital therapeutics analytics: Literature review and research directions,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 1, pp. 42–66, Jan. 2023. doi: 10.1109/JAS.2023.123015
    [62]
    F.-Y. Wang, “Parallel control and management for intelligent transportation systems: Concepts, architectures, and applications,” IEEE Trans. Intell. Transp. Syst., vol. 11, no. 3, pp. 630–638, Sept. 2010. doi: 10.1109/TITS.2010.2060218
    [63]
    X. Wang, J. Li, L. Fan, Y. Wang, and Y. Li, “Advancing vehicular healthcare: The DAO-based parallel maintenance for intelligent vehicles,” IEEE Trans. Intell. Veh., vol. 8, no. 12, pp. 4671–4673, Dec. 2023. doi: 10.1109/TIV.2023.3341855
    [64]
    J. Yang, Q. Ni, G. Luo, Q. Cheng, L. Oukhellou, and S. Han, “A trustworthy internet of vehicles: The DAO to safe, secure, and collaborative autonomous driving,” IEEE Trans. Intell. Veh., vol. 8, no. 12, pp. 4678–4681, Dec. 2023. doi: 10.1109/TIV.2023.3337345
    [65]
    J. Zhu, F.-Y. Wang, G. Wang, Y. L. Tian, Y. Yuan, X. Wang, H. W. Qi, and X. F. Jia, “Federated control: A distributed control approach towards information security and rights protection,” Acta Autom. Sinica, vol. 47, no. 8, pp. 1912–1920, Aug. 2021.
    [66]
    J. Zhu, Y. Yuan, F.-Y. Wang, and G. Wang, “Federated control: A trustable control framework for large-scale cyber-physical systems,” IEEE Trans. Ind. Inf., vol. 20, no. 5, pp. 7986–7994, May 2024. doi: 10.1109/TII.2024.3363092
    [67]
    Y. Zhao, H. Cao, Z. Zhu, S. Qiu, B. Chen, Peng Jiao, and F.-Y. Wang, “Crowd sensing intelligence for ITS: Participants, methods, and stages,” IEEE Trans. Intell. Veh., vol. 8, no. 6, pp. 3541–3546, Jun. 2023. doi: 10.1109/TIV.2023.3284046
    [68]
    J. Tu, J. Fan, N. Tang, P. Wang, G. Li, X. Du, X. Jia, and S. Gao, “Unicorn: A unified multi-tasking model for supporting matching tasks in data integration,” Proc. ACM Manage. Data, vol. 1, no. 1, p. 84, May 2023.
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