{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T15:22:13Z","timestamp":1784215333765,"version":"3.55.0"},"reference-count":48,"publisher":"IEEE","license":[{"start":{"date-parts":[[2024,5,20]],"date-time":"2024-05-20T00:00:00Z","timestamp":1716163200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,5,20]],"date-time":"2024-05-20T00:00:00Z","timestamp":1716163200000},"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","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017610","name":"Shenzhen Science and Technology Innovation Program","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100017610","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,5,20]]},"DOI":"10.1109\/infocom52122.2024.10621105","type":"proceedings-article","created":{"date-parts":[[2024,8,12]],"date-time":"2024-08-12T17:25:41Z","timestamp":1723483541000},"page":"631-640","source":"Crossref","is-referenced-by-count":15,"title":["Federated Learning While Providing Model as a Service: Joint Training and Inference Optimization"],"prefix":"10.1109","author":[{"given":"Pengchao","family":"Han","sequence":"first","affiliation":[{"name":"Guangdong University of Technology,School of Information Engineering,Guangzhou,China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shiqiang","family":"Wang","sequence":"additional","affiliation":[{"name":"IBM T. J. Watson Research Center,NY,USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Jiao","sequence":"additional","affiliation":[{"name":"Tongji University,Department of Computer Science and Technology,Shanghai,China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianwei","family":"Huang","sequence":"additional","affiliation":[{"name":"Shenzhen Institute of Artificial Intelligence and Robotics for Society, The Chinese University of Hong Kong, Shenzhen,School of Science and Engineering,Shenzhen,China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","article-title":"Torchserve"},{"key":"ref2","article-title":"Tensorflow serving"},{"key":"ref3","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume-title":"Artificial intelligence and statistics","author":"McMahan","year":"2017"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1561\/2200000083"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2021.3118354"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM48880.2022.9796833"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3095077"},{"key":"ref8","first-page":"10351","article-title":"Towards understanding biased client selection in federated learning","volume-title":"International Conference on Artificial Intelligence and Statistics","author":"Cho"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM53939.2023.10228925"},{"key":"ref10","article-title":"Joint participation incentive and network pricing design for federated learning","author":"Ningning Ding","year":"2023","journal-title":"IEEE INFOCOM"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM53939.2023.10229029"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2021.3096846"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TCOMM.2021.3081746"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.3046509"},{"key":"ref15","first-page":"3407","article-title":"Clustered sampling: Low-variance and improved representativity for clients selection in federated learning","volume-title":"ICML","author":"Fraboni"},{"key":"ref16","article-title":"Achieving linear speedup with partial worker participation in non-iid federated learning","volume-title":"ICLR","author":"Yang"},{"key":"ref17","article-title":"On the convergence of fedavg on non-iid data","volume-title":"ICLR","author":"Li"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP40776.2020.9053740"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/twc.2022.3153495"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/ICC45041.2023.10279272"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2019.2904348"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM53939.2023.10228945"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2019.2944481"},{"key":"ref24","first-page":"3788","article-title":"Marina: Faster non-convex distributed learning with compression","volume-title":"ICML","author":"Gorbunov"},{"key":"ref25","article-title":"The convergence of sparsified gradient methods","volume":"31","author":"Alistarh","year":"2018","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"237","key":"ref26","first-page":"1","article-title":"The error-feedback framework: Better rates for sgd with delayed gradients and compressed communication","volume":"21","author":"Stich","year":"2020","journal-title":"Journal of Machine Learning Research"},{"key":"ref27","article-title":"Network adaptive federated learning: Congestion and lossy compression","volume-title":"INFOCOM","author":"Parikshit Hegde"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM53939.2023.10229017"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/LWC.2022.3149783"},{"key":"ref30","article-title":"Qsgd: Communication-efficient sgd via gradient quantization and encoding","volume":"30","author":"Alistarh","year":"2017","journal-title":"Advances in neural information processing systems"},{"key":"ref31","first-page":"2021","article-title":"Fedpaq: A communication-efficient federated learning method with periodic averaging and quantization","volume-title":"International Conference on Artificial Intelligence and Statistics","author":"Reisizadeh"},{"key":"ref32","article-title":"Communication-efficient federated learning for heterogeneous edge devices based on adaptive gradient quantization","author":"Heting Liu","year":"2023","journal-title":"IEEE INFOCOM"},{"key":"ref33","article-title":"Fedlite: A scalable approach for federated learning on resource-constrained clients","author":"Wang","year":"2022"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/TNET.2018.2873606"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOMWKSHPS51825.2021.9484640"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM53939.2023.10229070"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2021.3118436"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-79995-2"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1007\/s00186-007-0161-1"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/TNSE.2022.3148276"},{"key":"ref41","first-page":"5132","article-title":"SCAFFOLD: Stochastic controlled averaging for federated learning","volume-title":"ICML","volume":"119","author":"Karimireddy"},{"key":"ref42","author":"Reddi","year":"2021","journal-title":"Adaptive Federated Optimization"},{"key":"ref43","article-title":"Fedexp: Speeding up federated averaging via extrapolation","volume-title":"ICLR","author":"Jhunjhunwala"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33015693"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM48880.2022.9796818"},{"key":"ref46","article-title":"Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms","author":"Xiao","year":"2017"},{"key":"ref47","article-title":"Reading digits in natural images with unsupervised feature learning","author":"Netzer","year":"2011"},{"key":"ref48","article-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2009"}],"event":{"name":"IEEE INFOCOM 2024 - IEEE Conference on Computer Communications","location":"Vancouver, BC, Canada","start":{"date-parts":[[2024,5,20]]},"end":{"date-parts":[[2024,5,23]]}},"container-title":["IEEE INFOCOM 2024 - IEEE Conference on Computer Communications"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/10621050\/10621073\/10621105.pdf?arnumber=10621105","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,13]],"date-time":"2024-08-13T05:23:52Z","timestamp":1723526632000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10621105\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,20]]},"references-count":48,"URL":"https:\/\/doi.org\/10.1109\/infocom52122.2024.10621105","relation":{},"subject":[],"published":{"date-parts":[[2024,5,20]]}}}