{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T18:50:34Z","timestamp":1764874234490,"version":"3.46.0"},"reference-count":73,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"1","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"GRF, Research Grants Council of Hong Kong","award":["CityU 11202623","CityU 11206425"],"award-info":[{"award-number":["CityU 11202623","CityU 11206425"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. on Mobile Comput."],"published-print":{"date-parts":[[2026,1]]},"DOI":"10.1109\/tmc.2025.3599524","type":"journal-article","created":{"date-parts":[[2025,8,15]],"date-time":"2025-08-15T18:22:42Z","timestamp":1755282162000},"page":"1104-1121","source":"Crossref","is-referenced-by-count":0,"title":["Towards Accurate Training Time Estimation for On-Device Heterogeneous Federated Learning"],"prefix":"10.1109","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0149-9857","authenticated-orcid":false,"given":"Kun","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Computer Science, City University of Hong Kong, Hong Kong"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5457-6967","authenticated-orcid":false,"given":"Zimu","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Data Science, City University of Hong Kong, Hong Kong"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3296-3392","authenticated-orcid":false,"given":"Zhenjiang","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Computer Science, City University of Hong Kong, Hong Kong"}]}],"member":"263","reference":[{"key":"ref14","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":"ref15","first-page":"3","article-title":"A public domain dataset for human activity recognition using smartphones","volume-title":"Proc. Eur. Symp. Artif. Neural Netw. Comput. Intell. Mach. Learn.","author":"Anguita"},{"article-title":"Flower: A friendly federated learning research framework","year":"2020","author":"Beutel","key":"ref16"},{"key":"ref17","first-page":"622","article-title":"Neuralpower: Predict and deploy energy-efficient convolutional neural networks","volume-title":"Proc. Asian Conf. Mach. Learn.","author":"Cai"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2024.3445373"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1145\/3560905.3568544"},{"key":"ref20","first-page":"8265","article-title":"Efficient and robust asynchronous federated learning with stragglers","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Chen"},{"key":"ref21","first-page":"578","article-title":"TVM: An automated end-to-end optimizing compiler for deep learning","volume-title":"Proc. USENIX Symp. Operating Syst. Des. Implementation","author":"Chen"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/MICRO.2014.11"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113193"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1145\/3560905.3568503"},{"key":"ref25","first-page":"6855","article-title":"HeteroFL: Computation and communication efficient federated learning for heterogeneous clients","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Diao"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1145\/3498361.3539765"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01234-2_48"},{"key":"ref29","first-page":"12876","article-title":"FjORD: Fair and accurate federated learning under heterogeneous targets with ordered dropout","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Horvath"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1145\/3715014.3722075"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1145\/3495243.3560551"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1145\/3581791.3596852"},{"key":"ref33","first-page":"814","article-title":"PAPAYA: Practical, private, and scalable federated learning","volume-title":"Proc. Mach. Learn. Syst.","author":"Huba"},{"key":"ref34","first-page":"1","article-title":"Beyond data and model parallelism for deep neural networks","volume-title":"Proc. Mach. Learn. Syst.","author":"Jia"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/BigData.2018.8622396"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-020-01316-z"},{"key":"ref37","first-page":"19","article-title":"Oort: Efficient federated learning via guided participant selection","volume-title":"Proc. USENIX Symp. Operating Syst. Des. Implementation","author":"Lai"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1145\/3447993.3483278"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1145\/3485730.3485929"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1145\/3495243.3517017"},{"article-title":"FedMD: Heterogenous federated learning via model distillation","year":"2019","author":"Li","key":"ref41"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2020.2975749"},{"key":"ref43","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume-title":"Proc. Mach. Learn. Syst.","author":"Li"},{"key":"ref44","first-page":"380","article-title":"Path forward beyond simulators: Fast and accurate GPU execution time prediction for DNN workloads","volume-title":"Proc. IEEE\/ACM Int. Symp. Microarchit.","author":"Li"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1145\/3570361.3592524"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref47","first-page":"8558","article-title":"DARTS: Differentiable architecture search","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Liu"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539086"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2023.3319952"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2024.3376548"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2022.3176400"},{"key":"ref52","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":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/ICC.2019.8761315"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1145\/3372224.3419188"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1145\/3458864.3467681"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1145\/3596907"},{"key":"ref57","first-page":"2565","article-title":"Paleo: A performance model for deep neural networks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Qi"},{"key":"ref58","first-page":"16342","article-title":"ZeroFL: Efficient on-device training for federated learning with local sparsity","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Qiu"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i7.20778"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1145\/3643832.3661880"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/tmc.2025.3581534"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1145\/3560905.3568538"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3160699"},{"year":"2016","key":"ref65","article-title":"INA3221 power monitor"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1145\/3485730.3485946"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2024.3355764"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1145\/3636534.3690705"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1145\/3447993.3448625"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1145\/3498361.3538928"},{"article-title":"Speech commands: A dataset for limited-vocabulary speech recognition","year":"2018","author":"Warden","key":"ref71"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1109\/TC.2020.2994391"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2021.07.041"},{"article-title":"Asynchronous federated optimization","year":"2019","author":"Xie","key":"ref74"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1145\/3485730.3485937"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1145\/3298981"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1145\/3636534.3649375"},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.1109\/ton.2025.3560408"},{"key":"ref79","doi-asserted-by":"publisher","DOI":"10.1109\/TC.2015.2425889"},{"key":"ref80","doi-asserted-by":"publisher","DOI":"10.1145\/3458864.3467882"},{"key":"ref81","doi-asserted-by":"publisher","DOI":"10.1109\/IOTM.004.2100182"},{"key":"ref82","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW59228.2023.00535"},{"key":"ref83","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599311"},{"key":"ref84","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":"ref85","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2021.07.098"},{"key":"ref86","first-page":"12878","article-title":"Data-free knowledge distillation for heterogeneous federated learning","volume-title":"Proc. ACM Int. Conf. Mach. Learn.","author":"Zhu"}],"container-title":["IEEE Transactions on Mobile Computing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/7755\/11275706\/11126981.pdf?arnumber=11126981","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T18:39:45Z","timestamp":1764873585000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11126981\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1]]},"references-count":73,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.1109\/tmc.2025.3599524","relation":{},"ISSN":["1536-1233","1558-0660","2161-9875"],"issn-type":[{"type":"print","value":"1536-1233"},{"type":"electronic","value":"1558-0660"},{"type":"electronic","value":"2161-9875"}],"subject":[],"published":{"date-parts":[[2026,1]]}}}