{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T20:31:53Z","timestamp":1776889913966,"version":"3.51.2"},"reference-count":27,"publisher":"IEEE","license":[{"start":{"date-parts":[[2025,4,6]],"date-time":"2025-04-06T00:00:00Z","timestamp":1743897600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,4,6]],"date-time":"2025-04-06T00:00:00Z","timestamp":1743897600000},"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":[[2025,4,6]]},"DOI":"10.1109\/icassp49660.2025.10887672","type":"proceedings-article","created":{"date-parts":[[2025,3,12]],"date-time":"2025-03-12T13:52:43Z","timestamp":1741787563000},"page":"1-5","source":"Crossref","is-referenced-by-count":1,"title":["FedRPN: An Efficient Framework for Optimizing System Heterogeneity in Federated Learning"],"prefix":"10.1109","author":[{"given":"Baolu","family":"Xue","sequence":"first","affiliation":[{"name":"Nanjing University of Aeronautics and Astronautics,Nanjing,China"}]},{"given":"Hanyuan","family":"Zheng","sequence":"additional","affiliation":[{"name":"Nanjing University of Aeronautics and Astronautics,Nanjing,China"}]},{"given":"Jiale","family":"Zhang","sequence":"additional","affiliation":[{"name":"Yangzhou University,Yangzhou,China"}]},{"given":"Jiewen","family":"Liu","sequence":"additional","affiliation":[{"name":"Nanjing University of Aeronautics and Astronautics,Nanjing,China"}]},{"given":"Bing","family":"Chen","sequence":"additional","affiliation":[{"name":"Nanjing University of Aeronautics and Astronautics,Nanjing,China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-62328-8"},{"key":"ref2","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":"ref3","article-title":"Federated optimization: Distributed machine learning for on-device intelligence","author":"Kone\u010dn\u1ef3","year":"2016","journal-title":"CoRR"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1145\/3625558"},{"key":"ref5","article-title":"Heterofl: Computation and communication efficient federated learning for heterogeneous clients","volume-title":"9th International Conference on Learning Representations, ICLR 2021","author":"Diao"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1145\/3447993.3483278"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i6.20555"},{"key":"ref8","first-page":"4270","article-title":"Resource-adaptive federated learning with all-in-one neural composition","volume":"35","author":"Mei","year":"2022","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.02350"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583347"},{"key":"ref11","article-title":"Federated learning with non-iid data","author":"Zhao","year":"2018"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1561\/2200000083"},{"key":"ref13","first-page":"562","article-title":"Deeply-supervised nets","volume-title":"Artificial intelligence and statistics","author":"Lee","year":"2015"},{"key":"ref14","first-page":"3301","article-title":"Shallow-deep networks: Understanding and mitigating network overthinking","volume-title":"International conference on machine learning","author":"Kaya"},{"key":"ref15","first-page":"232","article-title":"Infinite mixture prototypes for few-shot learning","volume-title":"International conference on machine learning","author":"Allen"},{"key":"ref16","article-title":"Guiding the last layer in federated learning with pre-trained models","volume":"36","author":"Legate","year":"2024","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref17","article-title":"On the importance and applicability of pre-training for federated learning","volume-title":"The Eleventh International Conference on Learning Representations, ICLR 2023","author":"Chen"},{"key":"ref18","article-title":"Where to begin? on the impact of pre-training and initialization in federated learning","volume-title":"The Eleventh International Conference on Learning Representations, ICLR 2023","author":"Nguyen"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i6.25891"},{"key":"ref20","first-page":"5972","article-title":"No fear of heterogeneity: Classifier calibration for federated learning with non-iid data","volume":"34","author":"Luo","year":"2021","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1145\/3581783.3611966"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01565"},{"key":"ref23","article-title":"Flower: A friendly federated learning research framework","author":"Beutel","year":"2020"},{"key":"ref24","article-title":"Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms","author":"Xiao","year":"2017"},{"key":"ref25","author":"Krizhevsky","year":"2009","journal-title":"Learning multiple layers of features from tiny images"},{"key":"ref26","article-title":"Measuring the effects of non-identical data distribution for federated visual classification","author":"Hsu","year":"2019"},{"key":"ref27","first-page":"21111","article-title":"Virtual homogeneity learning: Defending against data heterogeneity in federated learning","volume-title":"International Conference on Machine Learning","author":"Tang"}],"event":{"name":"ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","location":"Hyderabad, India","start":{"date-parts":[[2025,4,6]]},"end":{"date-parts":[[2025,4,11]]}},"container-title":["ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/10887540\/10887541\/10887672.pdf?arnumber=10887672","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T05:26:59Z","timestamp":1774416419000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10887672\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,6]]},"references-count":27,"URL":"https:\/\/doi.org\/10.1109\/icassp49660.2025.10887672","relation":{},"subject":[],"published":{"date-parts":[[2025,4,6]]}}}