{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T17:36:38Z","timestamp":1774719398980,"version":"3.50.1"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2025,8,19]],"date-time":"2025-08-19T00:00:00Z","timestamp":1755561600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,8,19]],"date-time":"2025-08-19T00:00:00Z","timestamp":1755561600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Science and Technology Project of Hebei Education Department","award":["ZD2022102"],"award-info":[{"award-number":["ZD2022102"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cluster Comput"],"published-print":{"date-parts":[[2025,9]]},"DOI":"10.1007\/s10586-025-05184-5","type":"journal-article","created":{"date-parts":[[2025,8,19]],"date-time":"2025-08-19T11:31:38Z","timestamp":1755603098000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["FedDC: a federated learning framework to enhance gradient diversity and mitigate conflicts"],"prefix":"10.1007","volume":"28","author":[{"given":"Yazhi","family":"Liu","sequence":"first","affiliation":[]},{"given":"Haonan","family":"Xia","sequence":"additional","affiliation":[]},{"given":"Wei","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,8,19]]},"reference":[{"key":"5184_CR1","unstructured":"McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273\u20131282 (2017). PMLR"},{"key":"5184_CR2","doi-asserted-by":"crossref","unstructured":"El\u00a0Mokadem, R., Ben\u00a0Maissa, Y., El\u00a0Akkaoui, Z.: Extreme federated learning (XFL): a layer-wise approach. Cluster Comput. 1\u201314 (2024)","DOI":"10.1007\/s10586-023-04242-0"},{"key":"5184_CR3","doi-asserted-by":"crossref","unstructured":"Wang, Z., Fan, X., Qi, J., Wen, C., Wang, C., Yu, R.: Federated learning with fair averaging (2021). arXiv:2104.14937","DOI":"10.24963\/ijcai.2021\/223"},{"key":"5184_CR4","unstructured":"Zhao, Y., Li, M., Lai, L., Suda, N., Civin, D., Chandra, V.: Federated learning with non-IID data (2018). arXiv:1806.00582"},{"key":"5184_CR5","unstructured":"Balakrishnan, R., Li, T., Zhou, T., Himayat, N., Smith, V., Bilmes, J.: Diverse client selection for federated learning via submodular maximization. In: International Conference on Learning Representations (2022)"},{"key":"5184_CR6","first-page":"5824","volume":"33","author":"T Yu","year":"2020","unstructured":"Yu, T., Kumar, S., Gupta, A., Levine, S., Hausman, K., Finn, C.: Gradient surgery for multi-task learning. Adv. Neural Inf. Process. Syst. 33, 5824\u20135836 (2020)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"5184_CR7","unstructured":"Wang, Z., Tsvetkov, Y., Firat, O., Cao, Y.: Gradient vaccine: investigating and improving multi-task optimization in massively multilingual models (2020). arXiv:2010.05874"},{"key":"5184_CR8","doi-asserted-by":"publisher","first-page":"244","DOI":"10.1016\/j.future.2022.05.003","volume":"135","author":"X Ma","year":"2022","unstructured":"Ma, X., Zhu, J., Lin, Z., Chen, S., Qin, Y.: A state-of-the-art survey on solving non-IID data in federated learning. Futur. Gener. Comput. Syst. 135, 244\u2013258 (2022)","journal-title":"Futur. Gener. Comput. Syst."},{"key":"5184_CR9","doi-asserted-by":"crossref","unstructured":"Li, A., Sun, J., Li, P., Pu, Y., Li, H., Chen, Y.: Hermes: an efficient federated learning framework for heterogeneous mobile clients. In: Proceedings of the 27th Annual International Conference on Mobile Computing and Networking, pp. 420\u2013437 (2021)","DOI":"10.1145\/3447993.3483278"},{"key":"5184_CR10","unstructured":"Li, A., Sun, J., Wang, B., Duan, L., Li, S., Chen, Y., Li, H.: Lotteryfl: personalized and communication-efficient federated learning with lottery ticket hypothesis on non-IID datasets (2020). arXiv:2008.03371"},{"key":"5184_CR11","doi-asserted-by":"publisher","unstructured":"Liang, X., Shen, S., Liu, J., Pan, Z., Chen, E., Cheng, Y.: Variance reduced local SGD with lower communication complexity (2019). https:\/\/doi.org\/10.48550\/arXiv.1912.12844","DOI":"10.48550\/arXiv.1912.12844"},{"key":"5184_CR12","first-page":"21554","volume":"33","author":"A Reisizadeh","year":"2020","unstructured":"Reisizadeh, A., Farnia, F., Pedarsani, R., Jadbabaie, A.: Robust federated learning: the case of affine distribution shifts. Adv. Neural Inf. Process. Syst. 33, 21554\u201321565 (2020)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"5184_CR13","first-page":"7611","volume":"33","author":"J Wang","year":"2020","unstructured":"Wang, J., Liu, Q., Liang, H., Joshi, G., Poor, H.V.: Tackling the objective inconsistency problem in heterogeneous federated optimization. Adv. Neural Inf. Process. Syst. 33, 7611\u20137623 (2020)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"5184_CR14","first-page":"429","volume":"2","author":"T Li","year":"2020","unstructured":"Li, T., Sahu, A.K., Zaheer, M., Sanjabi, M., Talwalkar, A., Smith, V.: Federated optimization in heterogeneous networks. Proc. Mach. Learn. Syst. 2, 429\u2013450 (2020)","journal-title":"Proc. Mach. Learn. Syst."},{"key":"5184_CR15","unstructured":"Karimireddy, S.P., Kale, S., Mohri, M., Reddi, S., Stich, S., Suresh, A.T.: Scaffold: stochastic controlled averaging for federated learning. In: International Conference on Machine Learning, pp. 5132\u20135143 (2020). PMLR"},{"key":"5184_CR16","unstructured":"Acar, D.A.E., Zhao, Y., Navarro, R.M., Mattina, M., Whatmough, P.N., Saligrama, V.: Federated learning based on dynamic regularization (2021). arXiv:2111.04263"},{"key":"5184_CR17","unstructured":"Hsu, T.-M.H., Qi, H., Brown, M.: Measuring the effects of non-identical data distribution for federated visual classification (2019). arXiv:1909.06335"},{"key":"5184_CR18","unstructured":"Reddi, S., Charles, Z., Zaheer, M., Garrett, Z., Rush, K., Kone\u010dn\u1ef3, J., Kumar, S., McMahan, H.B.: Adaptive federated optimization (2020). arXiv:2003.00295"},{"issue":"2","key":"5184_CR19","doi-asserted-by":"publisher","first-page":"1188","DOI":"10.1109\/TWC.2020.3031503","volume":"20","author":"J Xu","year":"2020","unstructured":"Xu, J., Wang, H.: Client selection and bandwidth allocation in wireless federated learning networks: a long-term perspective. IEEE Trans. Wirel. Commun. 20(2), 1188\u20131200 (2020)","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"5184_CR20","doi-asserted-by":"publisher","first-page":"21811","DOI":"10.1109\/JIOT.2023.3299573","volume":"10","author":"L Fu","year":"2023","unstructured":"Fu, L., Zhang, H., Gao, G., Zhang, M., Liu, X.: Client selection in federated learning: principles, challenges, and opportunities. IEEE Internet Things J. 10, 21811\u201321819 (2023)","journal-title":"IEEE Internet Things J."},{"issue":"2","key":"5184_CR21","doi-asserted-by":"publisher","first-page":"1129","DOI":"10.1007\/s11831-023-10011-4","volume":"31","author":"S Mayhoub","year":"2024","unstructured":"Mayhoub, S., Shami, M.T.: A review of client selection methods in federated learning. Arch. Comput. Methods Eng. 31(2), 1129\u20131152 (2024)","journal-title":"Arch. Comput. Methods Eng."},{"issue":"6","key":"5184_CR22","doi-asserted-by":"publisher","first-page":"4723","DOI":"10.1109\/JIOT.2020.3028742","volume":"8","author":"S AbdulRahman","year":"2020","unstructured":"AbdulRahman, S., Tout, H., Mourad, A., Talhi, C.: FedMCCS: multicriteria client selection model for optimal IoT federated learning. IEEE Internet Things J. 8(6), 4723\u20134735 (2020)","journal-title":"IEEE Internet Things J."},{"key":"5184_CR23","doi-asserted-by":"crossref","unstructured":"Tang, B., Xiao, Y., Zhang, L., Cao, B., Tang, M., Yang, Q.: AFL-HCS: asynchronous federated learning based on heterogeneous edge client selection. Cluster Comput. 1\u201318 (2024)","DOI":"10.1007\/s10586-024-04314-9"},{"key":"5184_CR24","doi-asserted-by":"crossref","unstructured":"Jiang, Z., Xu, Y., Xu, H., Wang, Z., Qian, C.: Heterogeneity-aware federated learning with adaptive client selection and gradient compression. In: IEEE INFOCOM 2023-IEEE Conference on Computer Communications, pp. 1\u201310 (2023). IEEE","DOI":"10.1109\/INFOCOM53939.2023.10229029"},{"key":"5184_CR25","unstructured":"Fraboni, Y., Vidal, R., Kameni, L., Lorenzi, M.: Clustered sampling: low-variance and improved representativity for clients selection in federated learning. In: International Conference on Machine Learning, pp. 3407\u20133416 (2021). PMLR"},{"key":"5184_CR26","doi-asserted-by":"crossref","unstructured":"Wang, Z., Fan, X., Qi, J., Jin, H., Yang, P., Shen, S., Wang, C.: FedGS: federated graph-based sampling with arbitrary client availability. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, pp. 10271\u201310278 (2023)","DOI":"10.1609\/aaai.v37i8.26223"},{"key":"5184_CR27","doi-asserted-by":"crossref","unstructured":"Tang, M., Ning, X., Wang, Y., Sun, J., Wang, Y., Li, H., Chen, Y.: FedCor: correlation-based active client selection strategy for heterogeneous federated learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10102\u201310111 (2022)","DOI":"10.1109\/CVPR52688.2022.00986"},{"key":"5184_CR28","unstructured":"Lopez-Paz, D., Ranzato, M.: Gradient episodic memory for continual learning. Adv. Neural Inf. Process. Syst. 30 (2017)"},{"key":"5184_CR29","unstructured":"Chaudhry, A., Ranzato, M., Rohrbach, M., Elhoseiny, M.: Efficient lifelong learning with a-gem (2018). arXiv:1812.00420"},{"key":"5184_CR30","unstructured":"Farajtabar, M., Azizan, N., Mott, A., Li, A.: Orthogonal gradient descent for continual learning. In: International Conference on Artificial Intelligence and Statistics, pp. 3762\u20133773 (2020). PMLR"},{"key":"5184_CR31","unstructured":"Alex, K.: Learning multiple layers of features from tiny images (2009). https:\/\/www.cs.toronto.edu\/kriz\/learning-features-2009-TR.pdf"},{"key":"5184_CR32","unstructured":"Xiao, H., Rasul, K., Vollgraf, R.: Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms (2017). arXiv:1708.07747"},{"key":"5184_CR33","unstructured":"Wang, Z., Fan, X., Peng, Z., Li, X., Yang, Z., Feng, M., Yang, Z., Liu, X., Wang, C.: Flgo: a fully customizable federated learning platform (2023). arXiv:2306.12079"},{"key":"5184_CR34","first-page":"280","volume":"1063","author":"YW Teh","year":"2010","unstructured":"Teh, Y.W., et al.: Dirichlet process. Encycl. Mach. Learn. 1063, 280\u2013287 (2010)","journal-title":"Encycl. Mach. Learn."}],"container-title":["Cluster Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-025-05184-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10586-025-05184-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-025-05184-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,15]],"date-time":"2025-09-15T19:06:40Z","timestamp":1757963200000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10586-025-05184-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,19]]},"references-count":34,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2025,9]]}},"alternative-id":["5184"],"URL":"https:\/\/doi.org\/10.1007\/s10586-025-05184-5","relation":{},"ISSN":["1386-7857","1573-7543"],"issn-type":[{"value":"1386-7857","type":"print"},{"value":"1573-7543","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,19]]},"assertion":[{"value":"18 July 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 January 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 February 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 August 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"We do not have any conflict of interest or Conflict of interest for this submission.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"No ethical approval was required for this research.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}],"article-number":"505"}}