{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T00:20:02Z","timestamp":1759969202939,"version":"build-2065373602"},"publisher-location":"New York, NY, USA","reference-count":21,"publisher":"ACM","license":[{"start":{"date-parts":[[2025,5,8]],"date-time":"2025-05-08T00:00:00Z","timestamp":1746662400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100006374","name":"Macquarie University","doi-asserted-by":"publisher","award":["20235578"],"award-info":[{"award-number":["20235578"]}],"id":[{"id":"10.13039\/501100006374","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,5,8]]},"DOI":"10.1145\/3701716.3715278","type":"proceedings-article","created":{"date-parts":[[2025,5,23]],"date-time":"2025-05-23T16:12:56Z","timestamp":1748016776000},"page":"713-716","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["A Fairer Client Selection Framework for Federated Internet of Things: Equality, Equity, and Trade-off Perspectives"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-7795-8710","authenticated-orcid":false,"given":"Noorain","family":"Mukhtiar","sequence":"first","affiliation":[{"name":"School of Computing, Macquarie University, Sydney, NSW, Australia"}]}],"member":"320","published-online":{"date-parts":[[2025,5,23]]},"reference":[{"key":"e_1_3_2_2_1_1","unstructured":"Annie Abay Yi Zhou Nathalie Baracaldo Shashank Rajamoni Ebube Chuba and Heiko Ludwig. 2020. Mitigating Bias in Federated Learning. arXiv (Preprint) arXiv:2012.02447."},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNSM.2023.3270168"},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i10.28971"},{"key":"e_1_3_2_2_4_1","volume-title":"Proceedings of the 36th International Conference on Neural Information Processing Systems. Curran Associates Inc., Article 415","author":"Chaudhury Bhaskar Ray","year":"2024","unstructured":"Bhaskar Ray Chaudhury, Linyi Li, Mintong Kang, Bo Li, and Ruta Mehta. 2024a. Fairness in Federated Learning via Core-stability. In Proceedings of the 36th International Conference on Neural Information Processing Systems. Curran Associates Inc., Article 415."},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.5555\/3692070.3693788"},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2024.3446864"},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.3390\/app131810258"},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.01148"},{"key":"e_1_3_2_2_9_1","unstructured":"Yuying Duan Yijun Tian Nitesh Chawla and Michael Lemmon. 2024. Post-Fair Federated Learning: Achieving Group and Community Fairness in Federated Learning via Post-processing. arXiv preprint arXiv:2405.17782."},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3652613"},{"key":"e_1_3_2_2_11_1","volume-title":"Proc. of the 41st International Conference on Machine Learning (ICML","author":"Liu Jiahao","year":"2024","unstructured":"Jiahao Liu, Yipeng Zhou, Di Wu, Miao Hu, Mohsen Guizani, and Quan Z. Sheng. 2024. FedLMT: Tackling System Heterogeneity of Federated Learning via Low-Rank Model Training with Theoretical Guarantees. In Proc. of the 41st International Conference on Machine Learning (ICML 2024)."},{"key":"e_1_3_2_2_12_1","volume-title":"Proceedings of the 36th International Conference on Machine Learning","volume":"97","author":"Mohri Mehryar","year":"2019","unstructured":"Mehryar Mohri, Gary Sivek, and Ananda Theertha Suresh. 2019. Agnostic Federated Learning. In Proceedings of the 36th International Conference on Machine Learning, Vol. 97. PMLR, 4615--4625."},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2021.3075439"},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/3544495"},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i13.29368"},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/3560905.3568305"},{"key":"e_1_3_2_2_17_1","first-page":"1","article-title":"Heterogeneous Federated Learning: State-of-the-art and Research Challenges","volume":"56","author":"Ye Mang","year":"2023","unstructured":"Mang Ye, Xiuwen Fang, Bo Du, Pong C Yuen, and Dacheng Tao. 2023. Heterogeneous Federated Learning: State-of-the-art and Research Challenges. ACM Computing Surveys, Vol. 56, 3 (2023), 1--44.","journal-title":"ACM Computing Surveys"},{"key":"e_1_3_2_2_18_1","unstructured":"Meiying Zhang Huan Zhao Sheldon Ebron Ruitao Xie and Kan Yang. 2023. Multi-criteria Client Selection and Scheduling with Fairness Guarantee for Federated Learning Service. arXiv (Preprint) arXiv:2312.14941."},{"volume-title":"Federated Learning for Internet of Things (SenSys '21)","author":"Zhang Tuo","key":"e_1_3_2_2_19_1","unstructured":"Tuo Zhang, Chaoyang He, Tianhao Ma, Lei Gao, Mark Ma, and Salman Avestimehr. 2021. Federated Learning for Internet of Things (SenSys '21). Association for Computing Machinery, New York, NY, USA, 413--419."},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/CIC50333.2020.00015"},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3589334.3645545"}],"event":{"name":"WWW '25: The ACM Web Conference 2025","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web"],"location":"Sydney NSW Australia","acronym":"WWW '25"},"container-title":["Companion Proceedings of the ACM on Web Conference 2025"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3701716.3715278","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3701716.3715278","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T03:06:36Z","timestamp":1759892796000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3701716.3715278"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,8]]},"references-count":21,"alternative-id":["10.1145\/3701716.3715278","10.1145\/3701716"],"URL":"https:\/\/doi.org\/10.1145\/3701716.3715278","relation":{},"subject":[],"published":{"date-parts":[[2025,5,8]]},"assertion":[{"value":"2025-05-23","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}