{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T16:48:37Z","timestamp":1782492517108,"version":"3.54.5"},"reference-count":54,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62472332"],"award-info":[{"award-number":["62472332"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004761","name":"Natural Science Foundation of Hainan Province","doi-asserted-by":"publisher","award":["621MS054"],"award-info":[{"award-number":["621MS054"]}],"id":[{"id":"10.13039\/501100004761","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004761","name":"Natural Science Foundation of Hainan Province","doi-asserted-by":"publisher","award":["526MS0269"],"award-info":[{"award-number":["526MS0269"]}],"id":[{"id":"10.13039\/501100004761","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Neural Networks"],"published-print":{"date-parts":[[2026,12]]},"DOI":"10.1016\/j.neunet.2026.109266","type":"journal-article","created":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T15:54:35Z","timestamp":1781711675000},"page":"109266","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Directly training on quantized model via gradient scale correction for edge device"],"prefix":"10.1016","volume":"204","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6208-755X","authenticated-orcid":false,"given":"Dewang","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingling","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chuang","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaohan","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Heping","family":"Gou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.neunet.2026.109266_bib0001","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/10494820.2024.2375338","article-title":"Discovering prerequisite relations using large language models","author":"Aytekin","year":"2024","journal-title":"Interactive Learning Environments"},{"key":"10.1016\/j.neunet.2026.109266_bib0002","doi-asserted-by":"crossref","first-page":"1405","DOI":"10.52202\/068431-0103","article-title":"Towards efficient post-training quantization of pre-trained language models","volume":"35","author":"Bai","year":"2022","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.neunet.2026.109266_bib0003","series-title":"Computer vision\u2013ECCV 2014: 13th European conference, Zurich, Switzerland, September 6-12, 2014, proceedings, part VI 13","first-page":"446","article-title":"Food-101\u2013mining discriminative components with random forests","author":"Bossard","year":"2014"},{"key":"10.1016\/j.neunet.2026.109266_bib0004","unstructured":"Brock, A., Lim, T., Ritchie, J. M., & Weston, N. (2017). Freezeout: Accelerate training by progressively freezing layers. arXiv preprint arXiv: 1706.04983."},{"key":"10.1016\/j.neunet.2026.109266_bib0005","unstructured":"Cai, H., Gan, C., Zhu, L., & Han, S. (2020). Tinytl: Reduce activations, not trainable parameters for efficient on-device learning. arXiv preprint arXiv: 2007.11622."},{"issue":"6","key":"10.1016\/j.neunet.2026.109266_bib0006","doi-asserted-by":"crossref","first-page":"1349","DOI":"10.1109\/JSAC.2019.2904360","article-title":"Toward knowledge as a service over networks: A deep learning model communication paradigm","volume":"37","author":"Chen","year":"2019","journal-title":"IEEE Journal on Selected Areas in Communications"},{"key":"10.1016\/j.neunet.2026.109266_bib0007","doi-asserted-by":"crossref","DOI":"10.1109\/TPAMI.2024.3447085","article-title":"A survey on deep neural network pruning: Taxonomy, comparison, analysis, and recommendations","author":"Cheng","year":"2024","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"10.1016\/j.neunet.2026.109266_bib0008","doi-asserted-by":"crossref","unstructured":"Chitsaz, K., Fournier, Q., Mordido, G., & Chandar, S. (2024). Exploring quantization for efficient pre-training of transformer language models. arXiv preprint arXiv: 2407.11722.","DOI":"10.18653\/v1\/2024.findings-emnlp.787"},{"key":"10.1016\/j.neunet.2026.109266_bib0009","series-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR)","first-page":"16174","article-title":"Reg-PTQ: Regression-specialized post-training quantization for fully quantized object detector","author":"Ding","year":"2024"},{"issue":"1","key":"10.1016\/j.neunet.2026.109266_bib0010","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1007\/s11263-014-0733-5","article-title":"The pascal visual object classes challenge: A retrospective","volume":"111","author":"Everingham","year":"2015","journal-title":"International Journal of Computer Vision"},{"key":"10.1016\/j.neunet.2026.109266_bib0011","series-title":"The eleventh international conference on learning representations","article-title":"Optq: Accurate quantization for generative pre-trained transformers","author":"Frantar","year":"2022"},{"key":"10.1016\/j.neunet.2026.109266_bib0012","series-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition","first-page":"1704","article-title":"QuantNAS: Quantization-aware neural architecture search for efficient deployment on mobile device","author":"Gao","year":"2024"},{"key":"10.1016\/j.neunet.2026.109266_bib0013","article-title":"From algorithm to hardware: A survey on efficient and safe deployment of deep neural networks","volume":"PP","author":"Geng","year":"2024","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"10.1016\/j.neunet.2026.109266_bib0014","unstructured":"Griffin, G., Holub, A., & Perona, P. (2022). Caltech 256. 10.22002\/D1.20087."},{"key":"10.1016\/j.neunet.2026.109266_bib0015","article-title":"Memory-efficient backpropagation through time","volume":"29","author":"Gruslys","year":"2016","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.neunet.2026.109266_bib0016","article-title":"La-loRA: Parameter-efficient fine-tuning with layer-wise adaptive low-rank adaptation","author":"Gu","year":"2025","journal-title":"Neural Networks"},{"key":"10.1016\/j.neunet.2026.109266_bib0017","article-title":"One-for-all: Bridge the gap between heterogeneous architectures in knowledge distillation","volume":"36","author":"Hao","year":"2024","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.neunet.2026.109266_bib0018","article-title":"1.1 Computing\u2019s energy problem (and what we can do about it)","author":"Horowitz","year":"2014","journal-title":"IEEE"},{"issue":"2","key":"10.1016\/j.neunet.2026.109266_bib0019","doi-asserted-by":"crossref","first-page":"2575","DOI":"10.1109\/TNNLS.2022.3190451","article-title":"Rct: Resource constrained training for edge ai","volume":"35","author":"Huang","year":"2022","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"10.1016\/j.neunet.2026.109266_bib0020","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.jpdc.2022.04.005","article-title":"Apt: The master-copy-free training method for quantised neural network on edge devices","volume":"166","author":"Huang","year":"2022","journal-title":"Journal of Parallel and Distributed Computing"},{"key":"10.1016\/j.neunet.2026.109266_bib0021","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.neunet.2022.06.026","article-title":"Compressing speaker extraction model with ultra-low precision quantization and knowledge distillation","volume":"154","author":"Huang","year":"2022","journal-title":"Neural Networks"},{"key":"10.1016\/j.neunet.2026.109266_bib0022","article-title":"Memory-efficient fine-tuning of compressed large language models via sub-4-bit integer quantization","volume":"36","author":"Kim","year":"2024","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.neunet.2026.109266_bib0023","unstructured":"Krizhevsky, A., Hinton, G. et al. (2009). Learning multiple layers of features from tiny images."},{"key":"10.1016\/j.neunet.2026.109266_bib0024","article-title":"Pruning vs quantization: Which is better?","volume":"36","author":"Kuzmin","year":"2024","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.neunet.2026.109266_bib0025","unstructured":"Kwon, Y. D., Li, R., Venieris, S., Chauhan, J., Lane, N. D., & Mascolo, C. (2023). Tinytrain: Deep neural network training at the extreme edge."},{"key":"10.1016\/j.neunet.2026.109266_bib0026","series-title":"Proceedings of the AAAI conference on artificial intelligence","first-page":"13355","article-title":"Owq: Outlier-aware weight quantization for efficient fine-tuning and inference of large language models","volume":"vol. 38","author":"Lee","year":"2024"},{"key":"10.1016\/j.neunet.2026.109266_bib0027","series-title":"Proceedings of the IEEE\/CVF international conference on computer vision","first-page":"5370","article-title":"Cluster-promoting quantization with bit-drop for minimizing network quantization loss","author":"Lee","year":"2021"},{"key":"10.1016\/j.neunet.2026.109266_bib0028","series-title":"Advances in neural information processing systems","first-page":"22941","article-title":"On-device training under 256KB memory","volume":"vol. 35","author":"Lin","year":"2022"},{"key":"10.1016\/j.neunet.2026.109266_bib0029","series-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition","first-page":"24427","article-title":"Pd-quant: Post-training quantization based on prediction difference metric","author":"Liu","year":"2023"},{"key":"10.1016\/j.neunet.2026.109266_bib0030","unstructured":"Liu, L., Deng, L., Hu, X., Zhu, M., Li, G., Ding, Y., & Xie, Y. (2018). Dynamic sparse graph for efficient deep learning. arXiv preprint arXiv: 1810.00859."},{"key":"10.1016\/j.neunet.2026.109266_bib0031","doi-asserted-by":"crossref","unstructured":"Luo, Y., Gao, Y., Zhang, Z., Fan, J., Zhang, H., & Xu, M. (2023). Long-range zero-shot generative deep network quantization. Neural Networks, 166, 683\u2013691.","DOI":"10.1016\/j.neunet.2023.07.042"},{"key":"10.1016\/j.neunet.2026.109266_bib0032","unstructured":"Nagel, M., Fournarakis, M., Amjad, R. A., Bondarenko, Y., Van Baalen, M., & Blankevoort, T. (2021). A white paper on neural network quantization. arXiv preprint arXiv: 2106.08295."},{"key":"10.1016\/j.neunet.2026.109266_bib0033","series-title":"2008 sixth indian conference on computer vision, graphics & image processing","first-page":"722","article-title":"Automated flower classification over a large number of classes","author":"Nilsback","year":"2008"},{"issue":"2","key":"10.1016\/j.neunet.2026.109266_bib0034","doi-asserted-by":"crossref","first-page":"599","DOI":"10.1109\/TC.2023.3337319","article-title":"Approximation-and quantization-aware training for graph neural networks","volume":"73","author":"Novkin","year":"2023","journal-title":"IEEE Transactions on Computers"},{"key":"10.1016\/j.neunet.2026.109266_bib0035","series-title":"Proceedings of the european conference on computer vision (ECCV)","first-page":"580","article-title":"Value-aware quantization for training and inference of neural networks","author":"Park","year":"2018"},{"key":"10.1016\/j.neunet.2026.109266_bib0036","doi-asserted-by":"crossref","DOI":"10.1016\/j.ins.2023.119972","article-title":"Towards real-world traffic prediction and data imputation: A multi-task pretraining and fine-tuning approach","volume":"657","author":"Qu","year":"2024","journal-title":"Information Sciences"},{"issue":"6","key":"10.1016\/j.neunet.2026.109266_bib0037","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3623402","article-title":"A comprehensive survey on model quantization for deep neural networks in image classification","volume":"14","author":"Rokh","year":"2023","journal-title":"ACM Transactions on Intelligent Systems and Technology"},{"key":"10.1016\/j.neunet.2026.109266_bib0038","unstructured":"Shen, X., Kong, Z., Yang, C., Han, Z., Lu, L., Dong, P., Lyu, C., Li, C.-h., Guo, X., Shu, Z. et al. (2024). Edgeqat: Entropy and distribution guided quantization-aware training for the acceleration of lightweight llms on the edge. arXiv preprint arXiv: 2402.10787."},{"key":"10.1016\/j.neunet.2026.109266_bib0039","article-title":"Enhancing quantization-aware training on edge devices via relative entropy coreset selection and cascaded layer correction","author":"Tong","year":"2026","journal-title":"IEEE Transactions on Mobile Computing"},{"key":"10.1016\/j.neunet.2026.109266_bib0040","article-title":"Matching networks for one shot learning","volume":"29","author":"Vinyals","year":"2016","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.neunet.2026.109266_bib0041","article-title":"Domain aware post training quantization for vision transformers in deployment","author":"Wang","year":"2025","journal-title":"Pattern Recognition"},{"key":"10.1016\/j.neunet.2026.109266_bib0042","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2023.126269","article-title":"Gradient distribution-aware INT8 training for neural networks","volume":"541","author":"Wang","year":"2023","journal-title":"Neurocomputing"},{"issue":"3","key":"10.1016\/j.neunet.2026.109266_bib0043","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1109\/MWC.008.2300516","article-title":"Device-edge cooperative fine-tuning of foundation models as a 6g service","volume":"31","author":"Wu","year":"2024","journal-title":"IEEE Wireless Communications"},{"key":"10.1016\/j.neunet.2026.109266_bib0044","series-title":"Ieee cvpr","first-page":"8787","article-title":"Ip102: A large-scale benchmark dataset for insect pest recognition","author":"Wu","year":"2019"},{"key":"10.1016\/j.neunet.2026.109266_bib0045","series-title":"Advances in neural information processing systems","first-page":"49146","article-title":"Training transformers with 4-bit integers","volume":"vol. 36","author":"Xi","year":"2023"},{"key":"10.1016\/j.neunet.2026.109266_bib0046","unstructured":"Xia, M., Gao, T., Zeng, Z., & Chen, D. (2023). Sheared llama: Accelerating language model pre-training via structured pruning. arXiv preprint arXiv: 2310.06694."},{"issue":"1","key":"10.1016\/j.neunet.2026.109266_bib0047","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1049\/cje.2020.11.002","article-title":"A review of FPGA-based custom computing architecture for convolutional neural network inference","volume":"30","author":"Xiyuan","year":"2021","journal-title":"Chinese Journal of Electronics"},{"key":"10.1016\/j.neunet.2026.109266_bib0048","article-title":"Hcdcmq: Hessian-aware channel determinism-decomposition with counterfactual multi-agent optimization for channel-wise mixed-precision post-training quantization","author":"Xu","year":"2026","journal-title":"Neurocomputing"},{"issue":"4","key":"10.1016\/j.neunet.2026.109266_bib0049","doi-asserted-by":"crossref","first-page":"778","DOI":"10.1109\/TEVC.2023.3252612","article-title":"Neural architecture search based on a multi-objective evolutionary algorithm with probability stack","volume":"27","author":"Xue","year":"2023","journal-title":"IEEE Transactions on Evolutionary Computation"},{"key":"10.1016\/j.neunet.2026.109266_bib0050","doi-asserted-by":"crossref","first-page":"27168","DOI":"10.52202\/068431-1970","article-title":"Zeroquant: Efficient and affordable post-training quantization for large-scale transformers","volume":"35","author":"Yao","year":"2022","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.neunet.2026.109266_bib0051","article-title":"Contrast-then-approximate: Analyzing keyword leakage of generative language models","author":"Zeng","year":"2024","journal-title":"IEEE Transactions on Information Forensics and Security"},{"key":"10.1016\/j.neunet.2026.109266_bib0052","series-title":"Proceedings of the AAAI conference on artificial intelligence","first-page":"3483","article-title":"Distribution adaptive int8 quantization for training cnns","volume":"vol. 35","author":"Zhao","year":"2021"},{"key":"10.1016\/j.neunet.2026.109266_bib0053","series-title":"International conference on learning representations","article-title":"Linear symmetric quantization of neural networks for low-precision integer hardware","author":"Zhao","year":"2020"},{"key":"10.1016\/j.neunet.2026.109266_bib0054","series-title":"European conference on computer vision","first-page":"56","article-title":"Pdt: Uav target detection dataset for pests and diseases tree","author":"Zhou","year":"2024"}],"container-title":["Neural Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0893608026007264?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0893608026007264?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T16:09:40Z","timestamp":1782490180000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0893608026007264"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,12]]},"references-count":54,"alternative-id":["S0893608026007264"],"URL":"https:\/\/doi.org\/10.1016\/j.neunet.2026.109266","relation":{},"ISSN":["0893-6080"],"issn-type":[{"value":"0893-6080","type":"print"}],"subject":[],"published":{"date-parts":[[2026,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Directly training on quantized model via gradient scale correction for edge device","name":"articletitle","label":"Article Title"},{"value":"Neural Networks","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neunet.2026.109266","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"109266"}}