{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T16:17:04Z","timestamp":1770740224111,"version":"3.49.0"},"reference-count":64,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"3","license":[{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62202273"],"award-info":[{"award-number":["62202273"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62302247"],"award-info":[{"award-number":["62302247"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Joint Key Funds of National Natural Science Foundation of China","award":["U24A20244"],"award-info":[{"award-number":["U24A20244"]}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2022QF140"],"award-info":[{"award-number":["ZR2022QF140"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2025MS1105"],"award-info":[{"award-number":["ZR2025MS1105"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Research and Development Project of Shandong Province","award":["2024CXPT052"],"award-info":[{"award-number":["2024CXPT052"]}]},{"name":"Shandong Science Foundation for Excellent Young Scholars","award":["2023HWYQ-007"],"award-info":[{"award-number":["2023HWYQ-007"]}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. on Mobile Comput."],"published-print":{"date-parts":[[2026,3]]},"DOI":"10.1109\/tmc.2025.3612427","type":"journal-article","created":{"date-parts":[[2025,9,22]],"date-time":"2025-09-22T17:46:23Z","timestamp":1758563183000},"page":"3051-3067","source":"Crossref","is-referenced-by-count":1,"title":["Unity is Power: Semi-Asynchronous Collaborative Training of Large-Scale Models With Structured Pruning in Resource-Limited Clients"],"prefix":"10.1109","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-7991-7485","authenticated-orcid":false,"given":"Yan","family":"Li","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Shandong University, Qingdao, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0824-9284","authenticated-orcid":false,"given":"Xiao","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Shandong University, Qingdao, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-9432-1068","authenticated-orcid":false,"given":"Mingyi","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Shandong University, Qingdao, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-0035-2732","authenticated-orcid":false,"given":"Guangwei","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Shandong University, Qingdao, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-8807-4097","authenticated-orcid":false,"given":"Feng","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Shandong University, Qingdao, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3976-0870","authenticated-orcid":false,"given":"Yuan","family":"Yuan","sequence":"additional","affiliation":[{"name":"School of Software & Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4579-5380","authenticated-orcid":false,"given":"Yifei","family":"Zou","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Shandong University, Qingdao, China"}]},{"given":"Mengying","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Shandong University, Qingdao, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7850-5568","authenticated-orcid":false,"given":"Jianbo","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Shandong University, Qingdao, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6835-5981","authenticated-orcid":false,"given":"Dongxiao","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Shandong University, Qingdao, China"}]}],"member":"263","reference":[{"key":"ref1","article-title":"GPT-4 technical report","author":"Achiam","year":"2023"},{"key":"ref2","article-title":"DeepSeek-V3 technical report","author":"Liu","year":"2024"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2025.103547"},{"key":"ref4","first-page":"1877","article-title":"Language models are few-shot learners","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Brown"},{"key":"ref5","first-page":"893","article-title":"Can foundation models perform zero-shot task specification for robot manipulation?","volume-title":"Proc. 4th Annu. Learn. Dyn. Control Conf.","author":"Cui"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.hcc.2024.100232"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-57959-7"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1007\/s11704-024-40065-x"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2023.3252029"},{"key":"ref10","first-page":"3622","article-title":"QuPeD: Quantized personalization via distillation with applications to federated learning","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Ozkara"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.00587"},{"key":"ref12","article-title":"Quantized distributed training of large models with convergence guarantees","author":"Markov","year":"2023"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.3233\/faia251320"},{"key":"ref14","article-title":"Adaptive control of client selection and gradient compression for efficient federated learning","author":"Jiang","year":"2022"},{"key":"ref15","article-title":"Federated learning of large models at the edge via principal sub-model training","author":"Niu","year":"2022"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i20.30209"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.hcc.2023.100164"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TETC.2023.3252600"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671865"},{"key":"ref20","first-page":"69069","article-title":"Thinking forward: Memory-efficient federated finetuning of language models","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Panchal"},{"key":"ref21","article-title":"Federated full-parameter tuning of billion-sized language models with communication cost under 18 kilobytes","author":"Qin","year":"2023"},{"key":"ref22","article-title":"Adaptive dynamic pruning for non-IID federated learning","author":"Yu","year":"2021"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599521"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3166101"},{"key":"ref25","article-title":"Federated learning with online adaptive heterogeneous local models","volume-title":"Proc. Workshop Federated Learn.: Recent Adv. New Challenges","author":"Zhou"},{"key":"ref26","article-title":"Efficient personalized federated learning via sparse model-adaptation","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Chen"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.02350"},{"key":"ref28","article-title":"Sparse random networks for communication-efficient federated learning","author":"Isik","year":"2022"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.03762"},{"key":"ref30","article-title":"Save it all: Enabling full parameter tuning for federated large language models via cycle block gradient descent","author":"Wang","year":"2024"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2025.naacl-long.424"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1145\/3485730.3485929"},{"key":"ref33","first-page":"695","article-title":"GlueFL: Reconciling client sampling and model masking for bandwidth efficient federated learning","volume-title":"Proc. Mach. Learn. Syst.","volume":"5","author":"He"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1016\/j.hcc.2025.100310"},{"key":"ref35","article-title":"Asynchronous federated optimization","author":"Xie","year":"2019"},{"key":"ref36","article-title":"Robust fully-asynchronous methods for distributed training over general architecture","author":"Zhu","year":"2023"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i12.29297"},{"key":"ref38","first-page":"3581","article-title":"Federated learning with buffered asynchronous aggregation","volume-title":"Proc. Int. Conf. Artif. Intell. Statist.","author":"Nguyen"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW59228.2023.00535"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1145\/3458817.3476211"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2023.3304788"},{"key":"ref42","article-title":"Deconstructing lottery tickets: Zeros, signs, and the supermask","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Zhou"},{"key":"ref43","volume-title":"An Introduction to Mathematical Probability","author":"Coolidge","year":"1925"},{"key":"ref44","article-title":"Estimating or propagating gradients through stochastic neurons for conditional computation","author":"Bengio","year":"2013"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00381"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.2024.3393169"},{"key":"ref47","first-page":"5677","article-title":"On the convergence of federated averaging with cyclic client participation","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Cho"},{"key":"ref48","article-title":"On the convergence of FedAvg on non-IID data","author":"Li","year":"2019"},{"key":"ref49","first-page":"10752","article-title":"On convergence of FedProx: Local dissimilarity invariant bounds, non-smoothness and beyond","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Yuan"},{"key":"ref50","first-page":"25991","article-title":"Every parameter matters: Ensuring the convergence of federated learning with dynamic heterogeneous models reduction","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Zhou"},{"key":"ref51","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume-title":"Proc. Int. Conf. Artif. Intell. Statist.","author":"McMahan"},{"key":"ref52","first-page":"26809","article-title":"PLATON: Pruning large transformer models with upper confidence bound of weight importance","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Zhang"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01199"},{"key":"ref54","article-title":"Q-ViT: Fully differentiable quantization for vision transformer","author":"Li","year":"2022"},{"key":"ref55","first-page":"29677","article-title":"FedRolex: Model-heterogeneous federated learning with rolling sub-model extraction","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Alam"},{"key":"ref56","article-title":"An image is worth 16 \u00d7 16 words: Transformers for image recognition at scale","author":"Dosovitskiy","year":"2020"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"ref58","article-title":"Measuring the effects of non-identical data distribution for federated visual classification","author":"Hsu","year":"2019"},{"key":"ref59","article-title":"NVIDIA FLARE: Federated learning from simulation to real-world","author":"Roth","year":"2022"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.2024.3423382"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1810.04805"},{"key":"ref62","article-title":"Llama 2: Open foundation and fine-tuned chat models","author":"Touvron","year":"2023"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.2139\/ssrn.4489826"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1002\/asi.23062"}],"container-title":["IEEE Transactions on Mobile Computing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/7755\/11372515\/11175042.pdf?arnumber=11175042","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,9]],"date-time":"2026-02-09T21:11:01Z","timestamp":1770671461000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11175042\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3]]},"references-count":64,"journal-issue":{"issue":"3"},"URL":"https:\/\/doi.org\/10.1109\/tmc.2025.3612427","relation":{},"ISSN":["1536-1233","1558-0660","2161-9875"],"issn-type":[{"value":"1536-1233","type":"print"},{"value":"1558-0660","type":"electronic"},{"value":"2161-9875","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3]]}}}