{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T16:06:37Z","timestamp":1772899597820,"version":"3.50.1"},"reference-count":292,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","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:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"name":"SmartSat CRC, whose activities are funded by Australian Government\u2019s CRC Program"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2026]]},"DOI":"10.1109\/access.2026.3664956","type":"journal-article","created":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T21:08:09Z","timestamp":1771276089000},"page":"33072-33104","source":"Crossref","is-referenced-by-count":0,"title":["Onboard Optimization and Learning: A Survey"],"prefix":"10.1109","volume":"14","author":[{"given":"Monirul Islam","family":"Pavel","sequence":"first","affiliation":[{"name":"School of Computer Science and Information Technology, The University of Adelaide, Adelaide, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3936-3568","authenticated-orcid":false,"given":"Siyi","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Computing and Mathematical Sciences, Curtin University, Perth, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6531-5087","authenticated-orcid":false,"given":"Mahardhika","family":"Pratama","sequence":"additional","affiliation":[{"name":"School of Computer Science and Information Technology, The University of Adelaide, Adelaide, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0937-4028","authenticated-orcid":false,"given":"Ryszard","family":"Kowalczyk","sequence":"additional","affiliation":[{"name":"School of Computer Science and Information Technology, The University of Adelaide, Adelaide, Australia"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1145\/3450494"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.3390\/s23031639"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2023.3297834"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2019.2946140"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2021.3084396"},{"key":"ref6","first-page":"11285","article-title":"Tinytl: Reduce memory, not parameters for efficient on-device learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Cai"},{"issue":"241","key":"ref7","first-page":"1","article-title":"Sparsity in deep learning: Pruning and growth for efficient inference and training in neural networks","volume":"22","author":"Hoefler","year":"2021","journal-title":"J. Mach. Learn. Res."},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3095077"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2020.2970550"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.2984887"},{"key":"ref11","first-page":"22941","article-title":"On-device training under 256kb memory","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"35","author":"Lin"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1145\/3696003"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TCAD.2022.3208187"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1145\/3524500"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.acl-long.107"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.3034925"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i3.16350"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.sysarc.2022.102431"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/DAC56929.2023.10247965"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.3390\/electronics14071345"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1145\/3649329.3658473"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TETC.2022.3223630"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TETCI.2024.3393367"},{"key":"ref24","first-page":"6377","article-title":"Pruning neural networks without any data by iteratively conserving synaptic flow","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Tanaka"},{"key":"ref25","first-page":"10323","article-title":"Sparsegpt: Massive language models can be accurately pruned in one-shot","volume-title":"Int. Conf. Mach. Learn.","author":"Frantar"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW60793.2023.00155"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2023.110386"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2023.3247798"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/TASE.2024.3407518"},{"key":"ref30","first-page":"1","article-title":"Delta keyword transformer: Bringing transformers to the edge through dynamically pruned multi-head self-attention","volume-title":"Proc. TinyML Res. Symp.","author":"Jel\u010dicov\u00e1"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2025.128930"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW54120.2021.00094"},{"key":"ref33","first-page":"24101","article-title":"A fast post-training pruning framework for transformers","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"35","author":"Kwon"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/tmc.2025.3629756"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01201"},{"key":"ref36","article-title":"Towards next-level post-training quantization of hyper-scale transformers","author":"Kim","year":"2024","journal-title":"arXiv:2402.08958"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-023-40770-4"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00529"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1145\/3776759.3776826"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2025.3609798"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00767"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58517-4_32"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00038"},{"key":"ref44","first-page":"11875","article-title":"Hawq-v3: Dyadic neural network quantization","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Yao"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00881"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/PRMVAI65741.2025.11108556"},{"key":"ref47","first-page":"5741","article-title":"Bayesian bits: Unifying quantization and pruning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Van Baalen"},{"key":"ref48","article-title":"FedHQ: Hybrid runtime quantization for federated learning","author":"Zheng","year":"2025","journal-title":"arXiv:2505.11982"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1145\/3695053.3731019"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1002\/spe.3422"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-021-01453-z"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3055564"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.117474"},{"key":"ref54","article-title":"Moonshine: Distilling with cheap convolutions","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"31","author":"Crowley"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/TTE.2024.3398991"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2021.3070013"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/HPCC-SmartCity-DSS50907.2020.00129"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2022.3157957"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/TBDATA.2022.3189703"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2022.3225185"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/JSTSP.2022.3224597"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/TNSE.2021.3054583"},{"key":"ref63","first-page":"27016","article-title":"Hardware-adaptive efficient latency prediction for nas via meta-learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Lee"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1109\/TC.2024.3449108"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1016\/j.jai.2022.100002"},{"key":"ref66","article-title":"DARTS: Differentiable architecture search","author":"Liu","year":"2018","journal-title":"arXiv:1806.09055"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01238"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1145\/3625687.3625814"},{"issue":"2","key":"ref69","first-page":"1","article-title":"LoRa: Low-rank adaptation of large language models","volume-title":"Proc. ICLR","volume":"1","author":"Hu"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.52202\/075280-0441"},{"key":"ref71","article-title":"LQ-LoRA: Low-rank plus quantized matrix decomposition for efficient language model finetuning","author":"Guo","year":"2023","journal-title":"arXiv:2311.12023"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2025.acl-long.99"},{"key":"ref73","article-title":"Low-rank quantization-aware training for LLMs","author":"Bondarenko","year":"2024","journal-title":"arXiv:2406.06385"},{"key":"ref74","first-page":"1950","article-title":"Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"35","author":"Liu"},{"key":"ref75","first-page":"1022","article-title":"Compacter: Efficient low-rank hypercomplex adapter layers","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Karimi Mahabadi"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-025-16794-9"},{"key":"ref77","first-page":"1381","article-title":"PockEngine: Sparse and efficient fine-tuning in a pocket","volume-title":"Proc. 56th Annu. IEEE\/ACM Int. Symp. Microarchitecture","author":"Zhu"},{"key":"ref78","article-title":"Tinytrain: Resource-aware task-adaptive sparse training of dnns at the data-scarce edge","volume-title":"Proc. 41st Int. Conf. Mach. Learn.","author":"Kwon"},{"key":"ref79","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN60899.2024.10651243"},{"key":"ref80","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.emnlp-main.243"},{"key":"ref81","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.eacl-main.39"},{"key":"ref82","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.emnlp-main.626"},{"key":"ref83","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2024.3447085"},{"key":"ref84","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2021.04.141"},{"key":"ref85","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2023.09.025"},{"key":"ref86","article-title":"Robust pruning at initialization","author":"Hayou","year":"2020","journal-title":"arXiv:2002.08797"},{"key":"ref87","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3166101"},{"key":"ref88","doi-asserted-by":"publisher","DOI":"10.1109\/ICC45041.2023.10278563"},{"key":"ref89","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/8039281"},{"key":"ref90","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.107636"},{"key":"ref91","doi-asserted-by":"publisher","DOI":"10.1109\/JSSC.2022.3174411"},{"key":"ref92","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00153"},{"key":"ref93","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01600"},{"key":"ref94","doi-asserted-by":"publisher","DOI":"10.1109\/WACV51458.2022.00099"},{"key":"ref95","doi-asserted-by":"publisher","DOI":"10.1109\/TC.2024.3449084"},{"key":"ref96","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.124599"},{"key":"ref97","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00286"},{"key":"ref98","article-title":"Integer quantization for deep learning inference: Principles and empirical evaluation","author":"Wu","year":"2020","journal-title":"arXiv:2004.09602"},{"key":"ref99","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2018.00221"},{"key":"ref100","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.2004.07320"},{"key":"ref101","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3172941"},{"key":"ref102","doi-asserted-by":"publisher","DOI":"10.1145\/3623402"},{"key":"ref103","first-page":"16318","article-title":"Overcoming oscillations in quantization-aware training","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Nagel"},{"key":"ref104","doi-asserted-by":"publisher","DOI":"10.52202\/068431-2443"},{"key":"ref105","doi-asserted-by":"publisher","DOI":"10.1145\/3699518"},{"key":"ref106","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00501"},{"key":"ref107","first-page":"13292","article-title":"Learning student-friendly teacher networks for knowledge distillation","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Park"},{"key":"ref108","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2024.3351239"},{"key":"ref109","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i2.25236"},{"key":"ref110","article-title":"NormKD: Normalized logits for knowledge distillation","author":"Chi","year":"2023","journal-title":"arXiv:2308.00520"},{"key":"ref111","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3014922"},{"key":"ref112","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2020.3044930"},{"key":"ref113","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2023.3297026"},{"key":"ref114","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2020.3047003"},{"key":"ref115","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume":"2","author":"Li","year":"2020","journal-title":"Proc. Mach. Learn. Syst."},{"key":"ref116","article-title":"ProxylessNAS: Direct neural architecture search on target task and hardware","author":"Cai","year":"2018","journal-title":"arXiv:1812.00332"},{"key":"ref117","article-title":"Once for all: Train one network and specialize it for efficient deployment","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Cai"},{"key":"ref118","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2024.120567"},{"key":"ref119","first-page":"11711","article-title":"Mcunet: Tiny deep learning on IoT devices","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Lin"},{"key":"ref120","doi-asserted-by":"publisher","DOI":"10.1109\/COINS65080.2025.11125772"},{"key":"ref121","doi-asserted-by":"publisher","DOI":"10.1145\/3638757"},{"key":"ref122","doi-asserted-by":"publisher","DOI":"10.1109\/MCAS.2023.3302182"},{"key":"ref123","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3207200"},{"key":"ref124","doi-asserted-by":"publisher","DOI":"10.1109\/IWQOS52092.2021.9521304"},{"key":"ref125","doi-asserted-by":"publisher","DOI":"10.1109\/ICDCS.2018.00154"},{"key":"ref126","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3292938"},{"key":"ref127","doi-asserted-by":"publisher","DOI":"10.3390\/s18092982"},{"key":"ref128","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2020.2995622"},{"key":"ref129","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2021.102974"},{"key":"ref130","doi-asserted-by":"publisher","DOI":"10.3390\/s21103523"},{"key":"ref131","doi-asserted-by":"publisher","DOI":"10.1109\/PERCOMW.2019.8730817"},{"key":"ref132","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2022.3222509"},{"key":"ref133","doi-asserted-by":"publisher","DOI":"10.1109\/ICDCS.2017.226"},{"key":"ref134","doi-asserted-by":"publisher","DOI":"10.1109\/LCOMM.2020.3034992"},{"key":"ref135","article-title":"Offloading algorithms for maximizing inference accuracy on edge device under a time constraint","author":"Fresa","year":"2021","journal-title":"arXiv:2112.11413"},{"key":"ref136","doi-asserted-by":"publisher","DOI":"10.1145\/3384419.3430898"},{"key":"ref137","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403058"},{"key":"ref138","doi-asserted-by":"publisher","DOI":"10.1109\/HPCC-SmartCity-DSS50907.2020.00078"},{"key":"ref139","doi-asserted-by":"publisher","DOI":"10.1109\/ISCA59077.2024.00019"},{"key":"ref140","doi-asserted-by":"publisher","DOI":"10.1145\/3669940.3707239"},{"key":"ref141","doi-asserted-by":"publisher","DOI":"10.1145\/3662006.3662066"},{"key":"ref142","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3244497"},{"key":"ref143","doi-asserted-by":"publisher","DOI":"10.1109\/HPCA56546.2023.10071121"},{"key":"ref144","doi-asserted-by":"publisher","DOI":"10.1109\/TCC.2023.3258982"},{"key":"ref145","doi-asserted-by":"publisher","DOI":"10.1145\/3093336.3037698"},{"key":"ref146","doi-asserted-by":"publisher","DOI":"10.1109\/TCAD.2018.2858384"},{"key":"ref147","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM.2019.8737614"},{"key":"ref148","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM41043.2020.9155237"},{"key":"ref149","doi-asserted-by":"publisher","DOI":"10.23919\/date64628.2025.10992692"},{"key":"ref150","doi-asserted-by":"publisher","DOI":"10.1145\/3469116.3470012"},{"key":"ref151","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR.2016.7900006"},{"key":"ref152","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107346"},{"key":"ref153","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i7.26042"},{"key":"ref154","doi-asserted-by":"publisher","DOI":"10.23919\/JCIN.2022.9815196"},{"key":"ref155","doi-asserted-by":"publisher","DOI":"10.3390\/info12100431"},{"key":"ref156","doi-asserted-by":"publisher","DOI":"10.1109\/TCAD.2023.3282579"},{"key":"ref157","doi-asserted-by":"publisher","DOI":"10.1109\/VTC2024-Spring62846.2024.10682995"},{"key":"ref158","first-page":"2516","article-title":"Zero time waste: Recycling predictions in early exit neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Wo\u0142czyk"},{"key":"ref159","doi-asserted-by":"publisher","DOI":"10.1109\/ICDS62089.2024.10756466"},{"key":"ref160","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.02350"},{"key":"ref161","doi-asserted-by":"publisher","DOI":"10.1145\/3514501"},{"key":"ref162","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2025.3583477"},{"key":"ref163","doi-asserted-by":"publisher","DOI":"10.1109\/TVLSI.2025.3543445"},{"key":"ref164","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOMWKSHPS47286.2019.9093772"},{"key":"ref165","doi-asserted-by":"publisher","DOI":"10.1145\/3372224.3419194"},{"key":"ref166","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2022.3172402"},{"key":"ref167","doi-asserted-by":"publisher","DOI":"10.1145\/3689632"},{"key":"ref168","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2020.2986024"},{"key":"ref169","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3038287"},{"key":"ref170","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2021.3095506"},{"key":"ref171","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2022.109490"},{"key":"ref172","doi-asserted-by":"publisher","DOI":"10.1145\/3466752.3480129"},{"key":"ref173","doi-asserted-by":"publisher","DOI":"10.1109\/TC.2023.3257510"},{"key":"ref174","doi-asserted-by":"publisher","DOI":"10.1109\/TNET.2023.3299851"},{"key":"ref175","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2019.2904348"},{"key":"ref176","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3093382"},{"key":"ref177","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2022.3221212"},{"key":"ref178","doi-asserted-by":"publisher","DOI":"10.1109\/TON.2025.3600015"},{"key":"ref179","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2025.3543295"},{"key":"ref180","doi-asserted-by":"publisher","DOI":"10.1016\/j.infsof.2024.107600"},{"key":"ref181","doi-asserted-by":"publisher","DOI":"10.1109\/DAC63849.2025.11133116"},{"key":"ref182","doi-asserted-by":"publisher","DOI":"10.3390\/app13031677"},{"key":"ref183","doi-asserted-by":"publisher","DOI":"10.3390\/fi17020087"},{"key":"ref184","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2024.3359040"},{"key":"ref185","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-024-53352-9"},{"key":"ref186","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.124564"},{"key":"ref187","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i8.20825"},{"key":"ref188","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2024.3367329"},{"key":"ref189","first-page":"15920","article-title":"Dark experience for general continual learning: A strong, simple baseline","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Buzzega"},{"key":"ref190","doi-asserted-by":"publisher","DOI":"10.1109\/TCAD.2023.3347640"},{"key":"ref191","doi-asserted-by":"publisher","DOI":"10.1109\/IROS45743.2020.9341460"},{"key":"ref192","doi-asserted-by":"publisher","DOI":"10.1109\/JETCAS.2021.3121554"},{"key":"ref193","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2022.3223944"},{"key":"ref194","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2025.3568279"},{"key":"ref195","first-page":"1407","article-title":"Impala: Scalable distributed deep-RL with importance weighted actor-learner architectures","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Espeholt"},{"key":"ref196","article-title":"SEED RL: Scalable and efficient deep-RL with accelerated central inference","author":"Espeholt","year":"2019","journal-title":"arXiv:1910.06591"},{"key":"ref197","first-page":"1","article-title":"Implementation matters in deep policy gradients: A case study on ppo and trpo","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Engstrom"},{"key":"ref198","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM42981.2021.9488733"},{"key":"ref199","article-title":"Low-rank agent-specific adaptation (LoRASA) for multi-agent policy learning","author":"Zhang","year":"2025","journal-title":"arXiv:2502.05573"},{"key":"ref200","article-title":"COMET: Co-optimization of a CNN model using efficient-hardware OBC techniques","author":"Chen","year":"2025","journal-title":"arXiv:2510.03516"},{"key":"ref201","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2024.3425569"},{"key":"ref202","doi-asserted-by":"publisher","DOI":"10.1109\/JETCAS.2023.3330428"},{"key":"ref203","doi-asserted-by":"publisher","DOI":"10.1109\/TVLSI.2019.2941921"},{"key":"ref204","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2019.2921977"},{"key":"ref205","doi-asserted-by":"publisher","DOI":"10.1007\/s10586-024-04686-y"},{"key":"ref206","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.011.2000246"},{"key":"ref207","article-title":"IDLG: Improved deep leakage from gradients","author":"Zhao","year":"2020","journal-title":"arXiv:2001.02610"},{"key":"ref208","doi-asserted-by":"publisher","DOI":"10.1145\/3704633"},{"key":"ref209","doi-asserted-by":"publisher","DOI":"10.1145\/2976749.2978318"},{"key":"ref210","doi-asserted-by":"publisher","DOI":"10.1145\/3133956.3133982"},{"key":"ref211","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3151670"},{"key":"ref212","doi-asserted-by":"publisher","DOI":"10.32604\/cmes.2025.063811"},{"key":"ref213","first-page":"54554","article-title":"Gan you see me? Enhanced data reconstruction attacks against split inference","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Li"},{"key":"ref214","doi-asserted-by":"publisher","DOI":"10.1145\/3456629"},{"key":"ref215","doi-asserted-by":"publisher","DOI":"10.1109\/SP40000.2020.00020"},{"key":"ref216","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2019.2918437"},{"key":"ref217","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-41110-7_6"},{"key":"ref218","doi-asserted-by":"publisher","DOI":"10.1109\/JETCAS.2021.3126816"},{"key":"ref219","doi-asserted-by":"publisher","DOI":"10.1145\/3319535.3363206"},{"key":"ref220","doi-asserted-by":"publisher","DOI":"10.1109\/JETCAS.2021.3074608"},{"key":"ref221","doi-asserted-by":"publisher","DOI":"10.1145\/3663673"},{"key":"ref222","doi-asserted-by":"publisher","DOI":"10.1109\/DAC.2018.8465773"},{"key":"ref223","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2020.04.034"},{"key":"ref224","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00756"},{"key":"ref225","doi-asserted-by":"publisher","DOI":"10.1109\/INOCON60754.2024.10511920"},{"key":"ref226","doi-asserted-by":"publisher","DOI":"10.3390\/s23094509"},{"key":"ref227","doi-asserted-by":"publisher","DOI":"10.1145\/3133956.3134012"},{"key":"ref228","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.3023126"},{"key":"ref229","first-page":"1","article-title":"Slalom: Fast, verifiable and private execution of neural networks in trusted hardware","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Tramer"},{"key":"ref230","doi-asserted-by":"publisher","DOI":"10.1109\/SP54263.2024.00052"},{"key":"ref231","doi-asserted-by":"publisher","DOI":"10.1145\/3523273"},{"key":"ref232","doi-asserted-by":"publisher","DOI":"10.1109\/SP46215.2023.10179382"},{"key":"ref233","doi-asserted-by":"publisher","DOI":"10.1109\/ICDCS54860.2022.00051"},{"key":"ref234","doi-asserted-by":"publisher","DOI":"10.1109\/TSC.2025.3607117"},{"key":"ref235","doi-asserted-by":"publisher","DOI":"10.1109\/SP46214.2022.9833743"},{"key":"ref236","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.001.2000196"},{"key":"ref237","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-025-01420-5"},{"key":"ref238","first-page":"4561","article-title":"Membership inference attacks and defenses in neural network pruning","volume-title":"Proc. 31st USENIX Secur. Symp.","author":"Yuan"},{"key":"ref239","first-page":"271","article-title":"ECQx: Explainability-driven quantization for low-bit and sparse DNNs","volume-title":"Proc. Int. Workshop Extending Explainable AI Beyond Deep Models Classifiers","author":"Becking"},{"key":"ref240","doi-asserted-by":"publisher","DOI":"10.1007\/s12559-024-10373-2"},{"key":"ref241","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.02333"},{"key":"ref242","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2020.08.011"},{"key":"ref243","doi-asserted-by":"publisher","DOI":"10.1145\/3678181"},{"key":"ref244","doi-asserted-by":"publisher","DOI":"10.1145\/3531146.3533235"},{"key":"ref245","doi-asserted-by":"publisher","DOI":"10.1109\/FUZZ52849.2023.10309783"},{"key":"ref246","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-024-00928-3"},{"key":"ref247","doi-asserted-by":"publisher","DOI":"10.1109\/OJCOMS.2025.3608784"},{"key":"ref248","first-page":"619","article-title":"Oblivious multi-party machine learning on trusted processors","volume-title":"Proc. 25th USENIX Secur. Symp.","author":"Ohrimenko"},{"key":"ref249","doi-asserted-by":"publisher","DOI":"10.1145\/3214292.3214301"},{"key":"ref250","doi-asserted-by":"publisher","DOI":"10.3390\/fi16100374"},{"key":"ref251","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2020.102117"},{"key":"ref252","doi-asserted-by":"publisher","DOI":"10.1515\/popets-2018-0024"},{"key":"ref253","first-page":"2003","article-title":"Cache telepathy: Leveraging shared resource attacks to learn DNN architectures","volume-title":"Proc. 29th USENIX Secur. Symp.","author":"Yan"},{"key":"ref254","doi-asserted-by":"publisher","DOI":"10.3390\/app14156699"},{"key":"ref255","doi-asserted-by":"publisher","DOI":"10.23919\/DATE48585.2020.9116340"},{"key":"ref256","doi-asserted-by":"publisher","DOI":"10.3390\/fi17020085"},{"key":"ref257","doi-asserted-by":"publisher","DOI":"10.1109\/OJCAS.2020.3047418"},{"key":"ref258","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2023.3326970"},{"key":"ref259","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-47748-5_18"},{"key":"ref260","doi-asserted-by":"publisher","DOI":"10.1109\/ICCAD57390.2023.10323746"},{"key":"ref261","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i8.20805"},{"key":"ref262","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2024.3515855"},{"key":"ref263","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2023.103278"},{"key":"ref264","first-page":"20366","article-title":"Sparcl: Sparse continual learning on the edge","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"35","author":"Wang"},{"key":"ref265","first-page":"5533","article-title":"Inducing and exploiting activation sparsity for fast inference on deep neural networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Kurtz"},{"key":"ref266","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i6.20611"},{"key":"ref267","first-page":"1","article-title":"Compacting, picking and growing for unforgetting continual learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Hung"},{"key":"ref268","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-022-04441-z"},{"key":"ref269","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2773081"},{"key":"ref270","doi-asserted-by":"publisher","DOI":"10.1109\/WACV45572.2020.9093365"},{"key":"ref271","first-page":"1093","article-title":"Prototype-sample relation distillation: Towards replay-free continual learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Asadi"},{"key":"ref272","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58604-1_43"},{"key":"ref273","first-page":"1240","article-title":"Online learned continual compression with adaptive quantization modules","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Caccia"},{"key":"ref274","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01640"},{"key":"ref275","doi-asserted-by":"publisher","DOI":"10.52202\/079017-1072"},{"key":"ref276","doi-asserted-by":"publisher","DOI":"10.1145\/3363554"},{"key":"ref277","doi-asserted-by":"publisher","DOI":"10.1109\/BigData55660.2022.10020597"},{"key":"ref278","doi-asserted-by":"publisher","DOI":"10.1145\/3597503.3639144"},{"key":"ref279","doi-asserted-by":"publisher","DOI":"10.1109\/CGO51591.2021.9370308"},{"key":"ref280","doi-asserted-by":"publisher","DOI":"10.1145\/3623278.3624767"},{"key":"ref281","doi-asserted-by":"publisher","DOI":"10.1002\/9781394219230.ch3"},{"key":"ref282","doi-asserted-by":"publisher","DOI":"10.1145\/3571133"},{"key":"ref283","doi-asserted-by":"publisher","DOI":"10.1145\/3444692"},{"key":"ref284","article-title":"MLPerf tiny benchmark","author":"Banbury","year":"2021","journal-title":"arXiv:2106.07597"},{"key":"ref285","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN64981.2025.11228997"},{"key":"ref286","doi-asserted-by":"publisher","DOI":"10.1109\/TCSI.2020.3021397"},{"key":"ref287","doi-asserted-by":"publisher","DOI":"10.1109\/JSSC.2024.3522304"},{"key":"ref288","doi-asserted-by":"publisher","DOI":"10.1109\/TCSII.2022.3179229"},{"key":"ref289","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-032-00071-2_21"},{"key":"ref290","first-page":"1","article-title":"Graph neural networks automated design and deployment on device-edge co-inference systems","volume-title":"Proc. 61st ACM\/IEEE Design Autom. Conf.","author":"Zhou"},{"key":"ref291","doi-asserted-by":"publisher","DOI":"10.1109\/IPDPS53621.2022.00111"},{"key":"ref292","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA48891.2023.10160358"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6287639\/11323511\/11396623.pdf?arnumber=11396623","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T21:03:22Z","timestamp":1772831002000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11396623\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":292,"URL":"https:\/\/doi.org\/10.1109\/access.2026.3664956","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]}}}