{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T10:07:02Z","timestamp":1775815622065,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":29,"publisher":"ACM","license":[{"start":{"date-parts":[[2025,4,22]],"date-time":"2025-04-22T00:00:00Z","timestamp":1745280000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Shenzhen Science and Technology Program","award":["KJZD20230923114809020"],"award-info":[{"award-number":["KJZD20230923114809020"]}]},{"name":"Research Team Cultivation Program of Shenzhen University","award":["2023QNT015"],"award-info":[{"award-number":["2023QNT015"]}]},{"DOI":"10.13039\/https:\/\/doi.org\/10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U23B2026, 62372305, 62302314"],"award-info":[{"award-number":["U23B2026, 62372305, 62302314"]}],"id":[{"id":"10.13039\/https:\/\/doi.org\/10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["2024B1515040012"],"award-info":[{"award-number":["2024B1515040012"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,4,28]]},"DOI":"10.1145\/3696410.3714961","type":"proceedings-article","created":{"date-parts":[[2025,4,22]],"date-time":"2025-04-22T22:52:18Z","timestamp":1745362338000},"page":"4432-4441","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Dealing with Noisy Data in Federated Learning: An Incentive Mechanism with Flexible Pricing"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3334-8308","authenticated-orcid":false,"given":"Hengzhi","family":"Wang","sequence":"first","affiliation":[{"name":"College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-8399-6030","authenticated-orcid":false,"given":"Haoran","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-1716-0017","authenticated-orcid":false,"given":"Minghe","family":"Ma","sequence":"additional","affiliation":[{"name":"College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1991-290X","authenticated-orcid":false,"given":"Laizhong","family":"Cui","sequence":"additional","affiliation":[{"name":"College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China"}]}],"member":"320","published-online":{"date-parts":[[2025,4,22]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Proc. of ICML. 1062--1070","author":"Chen Pengfei","year":"2019","unstructured":"Pengfei Chen, Ben Ben Liao, Guangyong Chen, and Shengyu Zhang. 2019. Understanding and utilizing deep neural networks trained with noisy labels. In Proc. of ICML. 1062--1070."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00983"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2024.3418862"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.14778\/3685800.3685900"},{"key":"e_1_3_2_1_5_1","volume-title":"Proc. of NeurIPS. 7276--7286","author":"Johnson Tyler B","year":"2018","unstructured":"Tyler B Johnson and Carlos Guestrin. 2018. Training Deep Models Faster with Robust, Approximate Importance Sampling. In Proc. of NeurIPS. 7276--7286."},{"key":"e_1_3_2_1_6_1","volume-title":"Proc. of ICML. 2525--2534","author":"Katharopoulos Angelos","year":"2018","unstructured":"Angelos Katharopoulos and Fran\u00e7ois Fleuret. 2018. Not All Samples Are Created Equal: Deep Learning with Importance Sampling. In Proc. of ICML. 2525--2534."},{"key":"e_1_3_2_1_7_1","volume-title":"Proc. of OSDI. 19--35","author":"Lai Fan","year":"2021","unstructured":"Fan Lai, Xiangfeng Zhu, Harsha V Madhyastha, and Mosharaf Chowdhury. 2021. Oort: Efficient federated learning via guided participant selection. In Proc. of OSDI. 19--35."},{"key":"e_1_3_2_1_8_1","volume-title":"Jackel","author":"LeCun Yann","year":"1989","unstructured":"Yann LeCun, Bernhard E. Boser, John S. Denker, Donnie Henderson, Richard E. Howard, Wayne E. Hubbard, and Lawrence D. Jackel. 1989. Handwritten Digit Recognition with a Back-Propagation Network. In Proc. of NeurIPS. 396--404."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i4.28095"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539328"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01057"},{"key":"e_1_3_2_1_12_1","volume-title":"Proc. of ICLR.","author":"Li Xiang","year":"2020","unstructured":"Xiang Li, Kaixuan Huang, Wenhao Yang, Shusen Wang, and Zhihua Zhang. 2020. On the Convergence of FedAvg on Non-IID Data. In Proc. of ICLR."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2024.3373501"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSC.2024.3387734"},{"key":"e_1_3_2_1_15_1","volume-title":"Proc. of ICML. 3355--3364","author":"Ma Xingjun","year":"2018","unstructured":"Xingjun Ma, Yisen Wang, Michael E Houle, Shuo Zhou, Sarah Erfani, Shutao Xia, Sudanthi Wijewickrema, and James Bailey. 2018. Dimensionality-driven learning with noisy labels. In Proc. of ICML. 3355--3364."},{"key":"e_1_3_2_1_16_1","volume-title":"Proc. of AISTATS. 1273--1282","author":"McMahan Brendan","year":"2017","unstructured":"Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. 2017. Communication-Efficient Learning of Deep Networks from Decentralized Data. In Proc. of AISTATS. 1273--1282."},{"key":"e_1_3_2_1_17_1","volume-title":"Proc. of NuerIPS. 17811--17831","author":"Murhekar Aniket","year":"2023","unstructured":"Aniket Murhekar, Zhuowen Yuan, Bhaskar Ray Chaudhury, Bo Li, and Ruta Mehta. 2023. Incentives in Federated Learning: Equilibria, Dynamics, and Mechanisms for Welfare Maximization. In Proc. of NuerIPS. 17811--17831."},{"key":"e_1_3_2_1_18_1","volume-title":"Optimal Auction Design. Mathematics of operations research","author":"Myerson Roger B","year":"1981","unstructured":"Roger B Myerson. 1981. Optimal Auction Design. Mathematics of operations research, Vol. 6, 1 (1981), 58--73."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i7.20755"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i13.29365"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/1785414.1785439"},{"key":"e_1_3_2_1_22_1","volume-title":"Proc. of ICLR.","author":"Stich Sebastian U.","year":"2019","unstructured":"Sebastian U. Stich. 2019. Local SGD Converges Fast and Communicates Little. In Proc. of ICLR."},{"key":"e_1_3_2_1_23_1","volume-title":"Proc. of NeurIPS. 4452--4463","author":"Stich Sebastian U","year":"2018","unstructured":"Sebastian U Stich, Jean-Baptiste Cordonnier, and Martin Jaggi. 2018. Sparsified SGD with Memory. In Proc. of NeurIPS. 4452--4463."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2023.3336050"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR48806.2021.9412599"},{"key":"e_1_3_2_1_26_1","volume-title":"Proc. of ICLR.","author":"Wu Zhaoxuan","year":"2024","unstructured":"Zhaoxuan Wu, Mohammad Mohammadi Amiri, Ramesh Raskar, and Bryan Kian Hsiang Low. 2024. Incentive-Aware Federated Learning with Training-Time Model Rewards. In Proc. of ICLR."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00994"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3589334.3645341"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449888"}],"event":{"name":"WWW '25: The ACM Web Conference 2025","location":"Sydney NSW Australia","acronym":"WWW '25","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web"]},"container-title":["Proceedings of the ACM on Web Conference 2025"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3696410.3714961","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3696410.3714961","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:18:54Z","timestamp":1750295934000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3696410.3714961"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,22]]},"references-count":29,"alternative-id":["10.1145\/3696410.3714961","10.1145\/3696410"],"URL":"https:\/\/doi.org\/10.1145\/3696410.3714961","relation":{},"subject":[],"published":{"date-parts":[[2025,4,22]]},"assertion":[{"value":"2025-04-22","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}