{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T14:04:43Z","timestamp":1772546683788,"version":"3.50.1"},"reference-count":27,"publisher":"National Library of Serbia","issue":"3","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["ComSIS","COMPUT SCI INF SYST","COMPUT SCI INFORM SY","COMPUTER SCI INFORM","COMSIS J"],"published-print":{"date-parts":[[2023]]},"abstract":"<jats:p>With the proposed Federated Learning (FL) paradigm based on the idea of ?data available but invisible?, participating nodes which create or hold data can perform local model training in a distributed manner, then a global model can be trained only by continuously aggregating model parameters or intermediate results from different nodes, thereby achieving a balance between data privacy protection and data sharing. However, there are some challenges when deploying a FL model. First, there may be hierarchical associations between participating nodes, so that the datasets held by each node are no longer independent of each other. Secondly, due to the possible abnormal delay of data transmission, it can seriously influence the aggregation of model parameters. In response to the above challenges, this paper proposes a newly designed FL framework for the participating nodes with hierarchical associations. In this framework, we design an adaptive model parameter aggregation algorithm, which can dynamically decide the aggregation strategy according to the state of network connection between nodes in different layers. Additionally, we conduct a theoretical analysis of the convergence of the proposed FL framework based on a non-convex objective function. Finally, the experimental results show that the proposed framework can be well applied to applications in different network connections, and can achieve faster model convergence efficiency while ensuring the accuracy of the model prediction.<\/jats:p>","DOI":"10.2298\/csis220930026c","type":"journal-article","created":{"date-parts":[[2023,3,9]],"date-time":"2023-03-09T11:38:03Z","timestamp":1678361883000},"page":"1037-1060","source":"Crossref","is-referenced-by-count":4,"title":["A hierarchical federated learning model with adaptive model parameter aggregation"],"prefix":"10.2298","volume":"20","author":[{"given":"Zhuo","family":"Chen","sequence":"first","affiliation":[{"name":"College of Computer Science and Engineering, Chongqing University of Technology, Chongqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chuan","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Computer Science and Engineering, Chongqing University of Technology, Chongqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Software Engineering, Auburn University, Auburn, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1078","reference":[{"key":"ref1","doi-asserted-by":"crossref","unstructured":"K. M. Ahmed, A. Imteaj, M. H. Amini, Federated deep learning for heterogeneous edge computing, in: 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA), 2021, pp. 1146-1152.","DOI":"10.1109\/ICMLA52953.2021.00187"},{"key":"ref2","doi-asserted-by":"crossref","unstructured":"M. Chen, D. G\u00a8und\u00a8uz, K. Huang, W. Saad, M. Bennis, A. V. Feljan, H. V. Poor, Distributed learning in wireless networks: Recent progress and future challenges, IEEE Journal on Selected Areas in Communications 39 (12) (2021) 3579-3605.","DOI":"10.1109\/JSAC.2021.3118346"},{"key":"ref3","doi-asserted-by":"crossref","unstructured":"L. Chettri, R. Bera, A comprehensive survey on internet of things (iot) toward 5g wireless systems, IEEE Internet of Things Journal 7 (1) (2020) 16-32.","DOI":"10.1109\/JIOT.2019.2948888"},{"key":"ref4","doi-asserted-by":"crossref","unstructured":"W. Y. B. Lim, Z. Xiong, J. Kang, D. Niyato, C. Leung, C. Miao, X. Shen, When information freshness meets service latency in federated learning: A task-aware incentive scheme for smart industries, IEEE Transactions on Industrial Informatics 18 (1) (2022) 457-466.","DOI":"10.1109\/TII.2020.3046028"},{"key":"ref5","unstructured":"B. McMahan, E. Moore, D. Ramage, S. Hampson, B. A. y Arcas, Communication-efficient learning of deep networks from decentralized data, in: Artificial intelligence and statistics, PMLR, 2017, pp. 1273-1282."},{"key":"ref6","unstructured":"A. Hard, K. Rao, R. Mathews, S. Ramaswamy, F. Beaufays, S. Augenstein, H. Eichner, C. Kiddon, D. Ramage, Federated learning for mobile keyboard prediction, arXiv preprint arXiv:1811.03604 (2018)."},{"key":"ref7","doi-asserted-by":"crossref","unstructured":"J. Domingo-Ferrer, A. Blanco-Justicia, J. Manj\u00b4on, D. S\u00b4anchez, Secure and privacy-preserving federated learning via co-utility, IEEE Internet of Things Journal 9 (5) (2022) 3988-4000.","DOI":"10.1109\/JIOT.2021.3102155"},{"key":"ref8","unstructured":"X. Li, K. Huang,W. Yang, S.Wang, Z. Zhang, On the convergence of fedavg on non-iid data, in: 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020, OpenReview.net, 2020."},{"key":"ref9","unstructured":"C. Xie, S. Koyejo, I. Gupta, Asynchronous federated optimization, CoRR abs\/1903.03934 (2019). arXiv:1903.03934."},{"key":"ref10","doi-asserted-by":"crossref","unstructured":"L. Liu, J. Zhang, S. Song, K. B. Letaief, Client-edge-cloud hierarchical federated learning, in: ICC 2020 - 2020 IEEE International Conference on Communications (ICC), 2020, pp. 1-6.","DOI":"10.1109\/ICC40277.2020.9148862"},{"key":"ref11","doi-asserted-by":"crossref","unstructured":"Z.Wang, H. Xu, J. Liu, H. Huang, C. Qiao, Y. Zhao, Resource-efficient federated learning with hierarchical aggregation in edge computing, in: IEEE INFOCOM 2021 - IEEE Conference on Computer Communications, 2021, pp. 1-10.","DOI":"10.1109\/INFOCOM42981.2021.9488756"},{"key":"ref12","doi-asserted-by":"crossref","unstructured":"W. Y. B. Lim, J. S. Ng, Z. Xiong, J. Jin, Y. Zhang, D. Niyato, C. Leung, C. Miao, Decentralized edge intelligence: A dynamic resource allocation framework for hierarchical federated learning, IEEE Transactions on Parallel and Distributed Systems 33 (3) (2022) 536-550.","DOI":"10.1109\/TPDS.2021.3096076"},{"key":"ref13","unstructured":"A. K. Sahu, T. Li, M. Sanjabi, M. Zaheer, A. Talwalkar, V. Smith, On the convergence of federated optimization in heterogeneous networks, CoRR abs\/1812.06127 (2018). arXiv:1812.06127."},{"key":"ref14","doi-asserted-by":"crossref","unstructured":"B. Luo, X. Li, S. Wang, J. Huang, L. Tassiulas, Cost-effective federated learning design, in: IEEE INFOCOM 2021 - IEEE Conference on Computer Communications, 2021, pp. 1-10.","DOI":"10.1109\/INFOCOM42981.2021.9488679"},{"key":"ref15","doi-asserted-by":"crossref","unstructured":"Z. Chen, W. Liao, K. Hua, C. Lu, W. Yu, Towards asynchronous federated learning for heterogeneous edge-powered internet of things, Digital Communications and Networks 7 (3) (2021) 317-326.","DOI":"10.1016\/j.dcan.2021.04.001"},{"key":"ref16","doi-asserted-by":"crossref","unstructured":"H. Zhu, M. Yang, J. Kuang, H. Qian, Y. Zhou, Client selection for asynchronous federated learning with fairness consideration, in: 2022 IEEE International Conference on Communications Workshops (ICC Workshops), 2022, pp. 800-805.","DOI":"10.1109\/ICCWorkshops53468.2022.9814669"},{"key":"ref17","doi-asserted-by":"crossref","unstructured":"C. Chen, H. Xu,W.Wang, B. Li, B. Li, L. Chen, G. Zhang, Communication-efficient federated learning with adaptive parameter freezing, in: 2021 IEEE 41st International Conference on Distributed Computing Systems (ICDCS), 2021, pp. 1-11.","DOI":"10.1109\/ICDCS51616.2021.00010"},{"key":"ref18","doi-asserted-by":"crossref","unstructured":"J. Liu, H. Xu, L. Wang, Y. Xu, C. Qian, J. Huang, H. Huang, Adaptive asynchronous federated learning in resource-constrained edge computing, IEEE Transactions on Mobile Computing (2021) 1-1.","DOI":"10.1016\/j.comnet.2021.108429"},{"key":"ref19","unstructured":"J. Jin, J. Ren, Y. Zhou, L. Lyu, J. Liu, D. Dou, Accelerated federated learning with decoupled adaptive optimization, in: K. Chaudhuri, S. Jegelka, L. Song, C. Szepesvari, G. Niu, S. Sabato (Eds.), Proceedings of the 39th International Conference on Machine Learning, Vol. 162 of Proceedings of Machine Learning Research, PMLR, 2022, pp. 10298-10322."},{"key":"ref20","unstructured":"S. J. Reddi, Z. Charles, M. Zaheer, Z. Garrett, K. Rush, J. Kone\u02c7cn\u00b4y, S. Kumar, H. B. McMahan, Adaptive federated optimization, in: 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021, OpenReview.net, 2021."},{"key":"ref21","doi-asserted-by":"crossref","unstructured":"Y. Zhao, X. Gong, Quality-aware distributed computation for cost-effective non-convex and asynchronous wireless federated learning, in: 2021 19th International Symposium on Modeling and Optimization in Mobile, Ad hoc, and Wireless Networks (WiOpt), IEEE, 2021, pp. 1-8.","DOI":"10.23919\/WiOpt52861.2021.9589660"},{"key":"ref22","doi-asserted-by":"crossref","unstructured":"C. Zhou, J. Liu, J. Jia, J. Zhou, Y. Zhou, H. Dai, D. Dou, Efficient device scheduling with multi-job federated learning, in: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 36, 2022, pp. 9971-9979.","DOI":"10.1609\/aaai.v36i9.21235"},{"key":"ref23","doi-asserted-by":"crossref","unstructured":"Q. Li, Y. Diao, Q. Chen, B. He, Federated learning on non-iid data silos: An experimental study, in: 2022 IEEE 38th International Conference on Data Engineering (ICDE), IEEE, 2022, pp. 965-978.","DOI":"10.1109\/ICDE53745.2022.00077"},{"key":"ref24","unstructured":"Tensorflow, Tensorflow, https:\/\/www.tensorflow.org (2022)."},{"key":"ref25","doi-asserted-by":"crossref","unstructured":"S. Q. Zhang, J. Lin, Q. Zhang, A multi-agent reinforcement learning approach for efficient client selection in federated learning, Proceedings of the AAAI Conference on Artificial Intelligence 36 (8) (2022) 9091-9099. doi:10.1609\/aaai.v36i8.20894.","DOI":"10.1609\/aaai.v36i8.20894"},{"key":"ref26","doi-asserted-by":"crossref","unstructured":"B. Xu, W. Xia, W. Wen, P. Liu, H. Zhao, H. Zhu, Adaptive hierarchical federated learning over wireless networks, IEEE Transactions on Vehicular Technology 71 (2) (2022) 2070-2083.","DOI":"10.1109\/TVT.2021.3135541"},{"key":"ref27","doi-asserted-by":"crossref","unstructured":"W. Shi, S. Zhou, Z. Niu, M. Jiang, L. Geng, Joint device scheduling and resource allocation for latency constrained wireless federated learning, IEEE Transactions on Wireless Communications 20 (1) (2021) 453-467.","DOI":"10.1109\/TWC.2020.3025446"}],"container-title":["Computer Science and Information Systems"],"original-title":[],"language":"en","deposited":{"date-parts":[[2024,7,26]],"date-time":"2024-07-26T08:08:04Z","timestamp":1721981284000},"score":1,"resource":{"primary":{"URL":"https:\/\/doiserbia.nb.rs\/Article.aspx?ID=1820-02142300026C"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"references-count":27,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2023]]}},"URL":"https:\/\/doi.org\/10.2298\/csis220930026c","relation":{},"ISSN":["1820-0214","2406-1018"],"issn-type":[{"value":"1820-0214","type":"print"},{"value":"2406-1018","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]}}}