{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T15:55:10Z","timestamp":1788450910153,"version":"build-2803163510"},"reference-count":34,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,2,13]],"date-time":"2023-02-13T00:00:00Z","timestamp":1676246400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"State Grid Corporation of China Science and Technology Project \u201cResearch and application of industry chain finance key technology based on blockchain\u201d","award":["5211DS21NOOU"],"award-info":[{"award-number":["5211DS21NOOU"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>At present, some studies have combined federated learning with blockchain, so that participants can conduct federated learning tasks under decentralized conditions, sharing and aggregating model parameters. However, these schemes do not take into account the trusted supervision of federated learning and the case of malicious node attacks. This paper introduces the concept of a trusted computing sandbox to solve this problem. A federated learning multi-task scheduling mechanism based on a trusted computing sandbox is designed and a decentralized trusted computing sandbox composed of computing resources provided by each participant is constructed as a state channel. The training process of the model is carried out in the channel and the malicious behavior is supervised by the smart contract, ensuring the data privacy of the participant node and the reliability of the calculation during the training process. In addition, considering the resource heterogeneity of participant nodes, the deep reinforcement learning method was used in this paper to solve the resource scheduling optimization problem in the process of constructing the state channel. The proposed algorithm aims to minimize the completion time of the system and improve the efficiency of the system while meeting the requirements of tasks on service quality as much as possible. Experimental results show that the proposed algorithm has better performance than the traditional heuristic algorithm and meta-heuristic algorithm.<\/jats:p>","DOI":"10.3390\/s23042093","type":"journal-article","created":{"date-parts":[[2023,2,13]],"date-time":"2023-02-13T05:25:26Z","timestamp":1676265926000},"page":"2093","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["A Federated Learning Multi-Task Scheduling Mechanism Based on Trusted Computing Sandbox"],"prefix":"10.3390","volume":"23","author":[{"given":"Hongbin","family":"Liu","sequence":"first","affiliation":[{"name":"State Grid Corporation of China, Beijing 100031, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Han","family":"Zhou","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Network and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Chen","sequence":"additional","affiliation":[{"name":"State Grid Zhejiang Electric Power Co., LTD., Hangzhou 310007, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yong","family":"Yan","sequence":"additional","affiliation":[{"name":"State Grid Zhejiang Electric Power Co., LTD., Hangzhou 310007, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianping","family":"Huang","sequence":"additional","affiliation":[{"name":"State Grid Zhejiang Electric Power Co., LTD., Hangzhou 310007, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ao","family":"Xiong","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Network and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaojie","family":"Yang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Network and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiewei","family":"Chen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Network and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaoyong","family":"Guo","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Network and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,13]]},"reference":[{"key":"ref_1","unstructured":"Nakamoto, S. (2008). Bitcoin: A peer-to-peer electronic cash system. Decentralized Bus. Rev., 21260."},{"key":"ref_2","unstructured":"McMahan, B., Moore, E., Ramage, D., Hampson, S., and Arcas, B.A. (2017). Communication-efficient learning of deep networks from decentralized data. Artif. Intell. Stat., 1273\u20131282."},{"key":"ref_3","unstructured":"Praneeth, K.S., Kale, S., Mohri, M., Reddi, S.J., Stich, S.U., and Theertha, S.A. (2019). SCAFFOLD: Stochastic Controlled Averaging for Federated Learning. arXiv."},{"key":"ref_4","unstructured":"Li, T., Sahu, A.K., Zaheer, M., Sanjabi, M., Talwalkar, A., and Smith, V. (2020). Federated optimization in heterogeneous networks. Proc. Mach. Learn. Syst., 429\u2013450."},{"key":"ref_5","unstructured":"Acar, D.A., Zhao, Y., Navarro, R.M., Mattina, M., Whatmough, P.N., and Saligrama, V. (2021). Federated learning based on dynamic regularization. arXiv."},{"key":"ref_6","unstructured":"He, C., Tan, C., Tang, H., Qiu, S., and Liu, J. (2019). Central server free federated learning over single-sided trust social networks. arXiv."},{"key":"ref_7","unstructured":"Vanhaesebrouck, P., Bellet, A., and Tommasi, M. (2017). Decentralized collaborative learning of personalized models over networks. Artif. Intell. Stat., 509\u2013517."},{"key":"ref_8","unstructured":"Bellet, A., Guerraoui, R., Taziki, M., and Tommasi, M. (2018). Personalized and private peer-to-peer machine learning. Int. Conf. Artif. Intell. Stat., 473\u2013481."},{"key":"ref_9","unstructured":"Roy, A.G., Siddiqui, S., P\u00f6lsterl, S., Navab, N., and Wachinger, C. (2019). Braintorrent: A peer-to-peer environment for decentralized federated learning. arXiv."},{"key":"ref_10","unstructured":"Hu, C., Jiang, J., and Wang, Z. (2019). Decentralized federated learning: A segmented gossip approach. arXiv."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1109\/MNET.011.2000263","article-title":"A blockchain-based decentralized federated learning framework with committee consensus","volume":"35","author":"Li","year":"2020","journal-title":"IEEE Netw."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Pappas, C., Chatzopoulos, D., Lalis, S., and Vavalis, M. (2021, January 21\u201324). IPLS: A framework for decentralized federated learning. Proceedings of the 2021 IFIP Networking Conference, Espoo, Finland.","DOI":"10.23919\/IFIPNetworking52078.2021.9472790"},{"key":"ref_13","first-page":"8056","article-title":"Pipe-SGD: A decentralized pipelined SGD framework for distributed deep net training","volume":"31","author":"Li","year":"2018","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"352","DOI":"10.1504\/IJWGS.2018.095647","article-title":"Blockchain challenges and opportunities: A survey","volume":"14","author":"Zheng","year":"2018","journal-title":"Int. J. Web Grid Serv."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"64678","DOI":"10.1109\/ACCESS.2020.2984378","article-title":"State channel as a service based on a distributed and decentralized web","volume":"8","author":"Podgorelec","year":"2020","journal-title":"IEEE Access"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"5171","DOI":"10.1109\/JIOT.2020.2977383","article-title":"Decentralized privacy using blockchain-enabled federated learning in fog computing","volume":"7","author":"Qu","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1817","DOI":"10.1109\/JIOT.2020.3017377","article-title":"Privacy-preserving blockchain-based federated learning for IoT devices","volume":"8","author":"Zhao","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1363","DOI":"10.1016\/j.future.2013.02.003","article-title":"Towards a community-based, social network-driven framework for Web services management","volume":"29","author":"Yahyaoui","year":"2013","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"878","DOI":"10.1016\/j.procs.2016.05.278","article-title":"Modified round robin algorithm for resource allocation in cloud computing","volume":"85","author":"Pradhan","year":"2016","journal-title":"Procedia Comput. Sci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1006\/jpdc.1999.1581","article-title":"Dynamic mapping of a class of independent tasks onto heterogeneous computing systems","volume":"59","author":"Maheswaran","year":"1999","journal-title":"J. Parallel Distrib. Comput."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Yu, J., and Buyya, R. (2006). A budget constrained scheduling of workflow applications on utility grids using genetic algorithms. Workshop Work. Support -Large-Scale Sci., 1\u201310.","DOI":"10.1109\/WORKS.2006.5282330"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2793","DOI":"10.1007\/s10586-017-0893-5","article-title":"Energy-efficient and QoS-aware model based resource consolidation in cloud data centers","volume":"20","author":"Li","year":"2017","journal-title":"Clust. Comput."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1007\/s10586-020-03075-5","article-title":"A novel hybrid antlion optimization algorithm for multi-objective task scheduling problems in cloud computing environments","volume":"24","author":"Abualigah","year":"2021","journal-title":"Clust. Comput."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Strumberger, I., Tuba, M., Bacanin, N., and Tuba, E. (2019). Cloudlet scheduling by hybridized monarch butterfly optimization algorithm. J. Sens. Actuator Netw., 8.","DOI":"10.3390\/jsan8030044"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1007\/s11277-019-06566-w","article-title":"A multi-objective optimal task scheduling in cloud environment using cuckoo particle swarm optimization","volume":"109","author":"Prem","year":"2019","journal-title":"Wirel. Pers. Commun."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"3576","DOI":"10.1109\/JIOT.2020.3025015","article-title":"Cloud resource scheduling with deep reinforcement learning and imitation learning","volume":"8","author":"Guo","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"940","DOI":"10.1109\/TMC.2020.3017079","article-title":"Dynamic scheduling for stochastic edge-cloud computing environments using a3c learning and residual recurrent neural networks","volume":"21","author":"Tuli","year":"2020","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"6214","DOI":"10.1109\/JIOT.2019.2961707","article-title":"Cooperative computation offloading and resource allocation for blockchain-enabled mobile-edge computing: A deep reinforcement learning approach","volume":"7","author":"Feng","year":"2019","journal-title":"IEEE Internet Things J."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"e5654","DOI":"10.1002\/cpe.5654","article-title":"Task scheduling based on deep reinforcement learning in a cloud manufacturing environment","volume":"32","author":"Dong","year":"2020","journal-title":"Concurr. Comput. Pract. Exp."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"9399","DOI":"10.1109\/JIOT.2020.3007869","article-title":"Resource optimization for delay-tolerant data in blockchain-enabled IoT with edge computing: A deep reinforcement learning approach","volume":"7","author":"Li","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_31","first-page":"514","article-title":"Adrl: A hybrid anomaly-aware deep reinforcement learning-based resource scaling in clouds","volume":"32","author":"Buyya","year":"2020","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1016\/j.future.2020.02.018","article-title":"Q-learning based dynamic task scheduling for energy-efficient cloud computing","volume":"108","author":"Ding","year":"2020","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.future.2019.09.044","article-title":"Revised reinforcement learning based on anchor graph hashing for autonomous cell activation in cloud-RANs","volume":"104","author":"Sun","year":"2020","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_34","unstructured":"Weng, J., Chen, H., Yan, D., You, K., Duburcq, A., Zhang, M., Su, Y., Su, H., and Zhu, J. (2021). Tianshou: A highly modularized deep reinforcement learning library. arXiv."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/4\/2093\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:33:24Z","timestamp":1760121204000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/4\/2093"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,13]]},"references-count":34,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["s23042093"],"URL":"https:\/\/doi.org\/10.3390\/s23042093","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,13]]}}}