{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T23:18:20Z","timestamp":1782861500430,"version":"3.54.5"},"reference-count":38,"publisher":"IEEE","license":[{"start":{"date-parts":[[2025,6,22]],"date-time":"2025-06-22T00:00:00Z","timestamp":1750550400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,6,22]],"date-time":"2025-06-22T00:00:00Z","timestamp":1750550400000},"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"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,6,22]]},"DOI":"10.1109\/dac63849.2025.11132995","type":"proceedings-article","created":{"date-parts":[[2025,9,15]],"date-time":"2025-09-15T17:35:41Z","timestamp":1757957741000},"page":"1-7","source":"Crossref","is-referenced-by-count":2,"title":["PracMHBench: Re-evaluating Model-Heterogeneous Federated Learning Based on Practical Edge Device Constraints"],"prefix":"10.1109","author":[{"given":"Yuanchun","family":"Guo","sequence":"first","affiliation":[{"name":"Beijing University of Posts and Telecommunications,School of Computer Science,Beijing,China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bingyan","family":"Liu","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications,School of Computer Science,Beijing,China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yulong","family":"Sha","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications,School of Computer Science,Beijing,China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhensheng","family":"Xian","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications,School of Computer Science,Beijing,China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","volume-title":"Towards federated learning at scale: System design","author":"Bonawitz","year":"2019"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00107"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1145\/3494966"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1145\/3495243.3517017"},{"key":"ref5","article-title":"Heterofl: Computation and communication efficient federated learning for heterogeneous clients","author":"Diao","year":"2021","journal-title":"ICLR"},{"key":"ref6","article-title":"Fedrolex: Modelheterogeneous federated learning with rolling sub-model extraction","author":"Alam","year":"2022","journal-title":"NeurIPS"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i8.20819"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1145\/3498361.3538917"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1145\/3570361.3613277"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583212"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1145\/3560905.3568503"},{"key":"ref12","first-page":"12876","article-title":"Fjord: Fair and accurate federated learning under heterogeneous targets with ordered dropout","volume":"34","author":"Horvath","year":"2021","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref13","first-page":"14068","article-title":"Group knowledge transfer: Federated learning of large cnns at the edge","volume":"33","author":"He","year":"2020","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2022\/399"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2022\/385"},{"key":"ref16","volume-title":"Leaf: A benchmark for federated settings","author":"Caldas","year":"2018"},{"key":"ref17","article-title":"Fedeval: A holistic evaluation framework for federated learning","author":"Chai","year":"2020","journal-title":"arXiv preprint arXiv:2011.09655"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/SRDS51746.2020.00017"},{"key":"ref19","first-page":"11814","article-title":"Fedscale: Benchmarking model and system performance of federated learning at scale","volume-title":"International Conference on Machine Learning","author":"Lai"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE53745.2022.00077"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449851"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref23","first-page":"6105","article-title":"Efficientnet: Rethinking model scaling for convolutional neural networks","volume-title":"International conference on machine learning","author":"Tan"},{"key":"ref24","volume-title":"Mobilenets: Efficient convolutional neural networks for mobile vision applications","author":"Howard","year":"2017"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.4324\/9781410605337-29"},{"key":"ref26","volume-title":"Memory-adaptive depth-wise heterogenous federated learning","author":"Zhang","year":"2023"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539086"},{"key":"ref28","article-title":"Depthfl: Depthwise federated learning for heterogeneous clients","volume-title":"The Eleventh International Conference on Learning Representations","author":"Kim"},{"key":"ref29","article-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2009"},{"key":"ref30","article-title":"Character-level convolutional networks for text classification","volume":"28","author":"Zhang","year":"2015","journal-title":"Advances in neural information processing systems"},{"key":"ref31","article-title":"Tensorflow federated stack overflow dataset","year":"2019"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1145\/3458864.3467681"},{"key":"ref33","article-title":"A public domain dataset for human activity recognition using smartphones","volume":"3","author":"Anguita","year":"2013","journal-title":"Esann"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.03762"},{"key":"ref35","article-title":"Albert: A lite bert for self-supervised learning of language representations","author":"Lan","year":"2019","journal-title":"arXiv preprint arXiv:1909.11942"},{"key":"ref36","article-title":"Evaluating federated learning for human activity recognition","volume-title":"Workshop AI for Internet of Things, in conjunction with IJCAI-PRICAI 2020","author":"Ek"},{"key":"ref37","volume-title":"Ai benchmark for different devices","year":"2021"},{"key":"ref38","article-title":"How much ram is in smartphones","year":"2022"}],"event":{"name":"2025 62nd ACM\/IEEE Design Automation Conference (DAC)","location":"San Francisco, CA, USA","start":{"date-parts":[[2025,6,22]]},"end":{"date-parts":[[2025,6,25]]}},"container-title":["2025 62nd ACM\/IEEE Design Automation Conference (DAC)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/11132383\/11132091\/11132995.pdf?arnumber=11132995","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T05:24:58Z","timestamp":1758000298000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11132995\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,22]]},"references-count":38,"URL":"https:\/\/doi.org\/10.1109\/dac63849.2025.11132995","relation":{},"subject":[],"published":{"date-parts":[[2025,6,22]]}}}