{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,18]],"date-time":"2026-07-18T10:20:29Z","timestamp":1784370029530,"version":"3.55.0"},"reference-count":48,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2025,4,25]],"date-time":"2025-04-25T00:00:00Z","timestamp":1745539200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["92267206, 62032013"],"award-info":[{"award-number":["92267206, 62032013"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Internet Technol."],"published-print":{"date-parts":[[2025,5,31]]},"abstract":"<jats:p>\n            Federated learning stands out as a promising approach within the domain of edge computing, providing a framework for collaborative training on distributed datasets without necessitating data sharing. However, federated learning involves the frequent transmission of machine learning model updates between the server and clients, resulting in high communication costs. Additionally, heterogeneous clients can further complicate the Federated Learning process and deteriorate performance. To address these challenges, we propose\n            <jats:italic>Adaptive Self-Knowledge Distillation-based Quality- and Reputation-Aware Cross-Device Federated Learning<\/jats:italic>\n            (ASDQR) - an efficient communication and inference framework designed for heterogeneous clients. ASDQR initiates the process by selecting high-reputation and high-quality clients to be involved in federated learning, significantly impacting communication efficiency and inference effectiveness. ASDQR also introduces a model of adaptive local self-knowledge distillation that incorporates multiple local personalized historical knowledge for more accurate inference, allowing the historical level to be dynamically adjusted across time. Finally, we present an inference-effective aggregation scheme that assigns higher weights to important and reliable local model updates based on clients\u2019 contribution degrees when performing global model aggregation. ASDQR consistently outperforms baseline methods across all datasets and communication rounds, achieving 9.0% higher accuracy than FedAvg, 6.59% higher than MOON, 0.29% higher than FedProx, 0.2% higher than PFedSD, and 0.08% higher than FedMD on the MNIST dataset at 100 communication rounds. Similar improvements are observed on CIFAR, HAR, and WISDM datasets, demonstrating the robustness and efficiency of ASDQR in federated learning with non-IID data.\n          <\/jats:p>","DOI":"10.1145\/3716870","type":"journal-article","created":{"date-parts":[[2025,2,11]],"date-time":"2025-02-11T11:29:51Z","timestamp":1739273391000},"page":"1-37","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":18,"title":["Communication-Efficient Federated Learning for Heterogeneous Clients"],"prefix":"10.1145","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6585-0714","authenticated-orcid":false,"given":"Ying","family":"Li","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Northeastern University College of Computer Science and Engineering, Shenyang, China and TU Wien, Vienna, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5431-3828","authenticated-orcid":false,"given":"Xingwei","family":"Wang","sequence":"additional","affiliation":[{"name":"Northeastern University College of Computer Science and Engineering, Shenyang, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7921-1844","authenticated-orcid":false,"given":"Haodong","family":"Li","sequence":"additional","affiliation":[{"name":"Northeastern University College of Computer Science and Engineering, Shenyang, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8233-6071","authenticated-orcid":false,"given":"Praveen Kumar","family":"Donta","sequence":"additional","affiliation":[{"name":"Stockholm University, Stockholm, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3835-6681","authenticated-orcid":false,"given":"Min","family":"Huang","sequence":"additional","affiliation":[{"name":"Northeastern University, Shenyang, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6872-8821","authenticated-orcid":false,"given":"Schahram","family":"Dustdar","sequence":"additional","affiliation":[{"name":"Distributed Systems Group, TU Wien, Vienna, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,4,25]]},"reference":[{"key":"e_1_3_8_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.3028742"},{"key":"e_1_3_8_3_2","volume-title":"International Conference on Learning Representations","author":"Balakrishnan Ravikumar","year":"2022","unstructured":"Ravikumar Balakrishnan, Tian Li, Tianyi Zhou, Nageen Himayat, Virginia Smith, and Jeff Bilmes. 2022. Diverse client selection for federated learning via submodular maximization. In International Conference on Learning Representations."},{"key":"e_1_3_8_4_2","first-page":"560","volume-title":"International Conference on Machine Learning","author":"Bernstein Jeremy","year":"2018","unstructured":"Jeremy Bernstein, Yu-Xiang Wang, Kamyar Azizzadenesheli, and Animashree Anandkumar. 2018. signSGD: Compressed optimisation for non-convex problems. In International Conference on Machine Learning. PMLR, 560\u2013569."},{"key":"e_1_3_8_5_2","first-page":"1877","article-title":"Language models are few-shot learners","volume":"33","author":"Brown Tom","year":"2020","unstructured":"Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D. Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et\u00a0al. 2020. Language models are few-shot learners. Advances in Neural Information Processing Systems 33 (2020), 1877\u20131901.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_8_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.2019.1800608"},{"key":"e_1_3_8_7_2","article-title":"Focus: Dealing with label quality disparity in federated learning","author":"Chen Yiqiang","year":"2020","unstructured":"Yiqiang Chen, Xiaodong Yang, Xin Qin, Han Yu, Biao Chen, and Zhiqi Shen. 2020. Focus: Dealing with label quality disparity in federated learning. arXiv preprint arXiv:2001.11359 (2020).","journal-title":"arXiv preprint arXiv:2001.11359"},{"key":"e_1_3_8_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/LCOMM.2022.3140273"},{"key":"e_1_3_8_9_2","first-page":"10351","volume-title":"International Conference on Artificial Intelligence and Statistics","author":"Cho Yae Jee","year":"2022","unstructured":"Yae Jee Cho, Jianyu Wang, and Gauri Joshi. 2022. Towards understanding biased client selection in federated learning. In International Conference on Artificial Intelligence and Statistics. PMLR, 10351\u201310375."},{"key":"e_1_3_8_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM42981.2021.9488743"},{"key":"e_1_3_8_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2021.3134647"},{"key":"e_1_3_8_12_2","volume-title":"International Conference on Learning Representations","author":"Diao Enmao","year":"2020","unstructured":"Enmao Diao, Jie Ding, and Vahid Tarokh. 2020. HeteroFL: Computation and communication efficient federated learning for heterogeneous clients. In International Conference on Learning Representations."},{"key":"e_1_3_8_13_2","volume-title":"Proceedings of the 36th AAAI Conference on Artificial Intelligence, Virtual","volume":"22","author":"He Yuting","year":"2022","unstructured":"Yuting He, Yiqiang Chen, Xiaodong Yang, Yingwei Zhang, and Bixiao Zeng. 2022. Class-wise adaptive self distillation for heterogeneous federated learning. In Proceedings of the 36th AAAI Conference on Artificial Intelligence, Virtual, Vol. 22."},{"key":"e_1_3_8_14_2","first-page":"8852","volume-title":"International Conference on Machine Learning","author":"H\u00f6nig Robert","year":"2022","unstructured":"Robert H\u00f6nig, Yiren Zhao, and Robert Mullins. 2022. DAdaQuant: Doubly-adaptive quantization for communication-efficient Federated Learning. In International Conference on Machine Learning. PMLR, 8852\u20138866."},{"key":"e_1_3_8_15_2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2022.3172113"},{"key":"e_1_3_8_16_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2020.3040887"},{"key":"e_1_3_8_17_2","doi-asserted-by":"publisher","DOI":"10.1109\/VTC2020-Fall49728.2020.9348439"},{"key":"e_1_3_8_18_2","article-title":"Adaptive aggregation for federated learning","author":"Jayaram K. R.","year":"2022","unstructured":"K. R. Jayaram, Vinod Muthusamy, Gegi Thomas, Ashish Verma, and Mark Purcell. 2022. Adaptive aggregation for federated learning. arXiv preprint arXiv:2203.12163 (2022).","journal-title":"arXiv preprint arXiv:2203.12163"},{"key":"e_1_3_8_19_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2022.3225185"},{"key":"e_1_3_8_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2019.2940820"},{"key":"e_1_3_8_21_2","unstructured":"Alex Krizhevsky and Geoffrey Hinton. 2009. Learning multiple layers of features from tiny images. (2009)."},{"key":"e_1_3_8_22_2","article-title":"The MNIST database of handwritten digits","author":"LeCun Yann","year":"1998","unstructured":"Yann LeCun. 1998. The MNIST database of handwritten digits. http:\/\/yann. lecun. com\/exdb\/mnist\/ (1998).","journal-title":"http:\/\/yann. lecun. com\/exdb\/mnist\/"},{"key":"e_1_3_8_23_2","doi-asserted-by":"publisher","DOI":"10.1145\/3447993.3483278"},{"key":"e_1_3_8_24_2","doi-asserted-by":"publisher","DOI":"10.1145\/3495243.3517017"},{"key":"e_1_3_8_25_2","article-title":"FedMD: Heterogenous federated learning via model distillation","author":"Li Daliang","year":"2019","unstructured":"Daliang Li and Junpu Wang. 2019. FedMD: Heterogenous federated learning via model distillation. arXiv preprint arXiv:1910.03581 (2019).","journal-title":"arXiv preprint arXiv:1910.03581"},{"key":"e_1_3_8_26_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01057"},{"key":"e_1_3_8_27_2","article-title":"A survey on federated learning systems: Vision, hype and reality for data privacy and protection","author":"Li Qinbin","year":"2021","unstructured":"Qinbin Li, Zeyi Wen, Zhaomin Wu, Sixu Hu, Naibo Wang, Yuan Li, Xu Liu, and Bingsheng He. 2021. A survey on federated learning systems: Vision, hype and reality for data privacy and protection. IEEE Transactions on Knowledge and Data Engineering (2021).","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_8_28_2","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume":"2","author":"Li Tian","year":"2020","unstructured":"Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith. 2020. Federated optimization in heterogeneous networks. Proceedings of Machine Learning and Systems 2 (2020), 429\u2013450.","journal-title":"Proceedings of Machine Learning and Systems"},{"key":"e_1_3_8_29_2","article-title":"On the convergence of FedAvg on non-IID data","author":"Li Xiang","year":"2019","unstructured":"Xiang Li, Kaixuan Huang, Wenhao Yang, Shusen Wang, and Zhihua Zhang. 2019. On the convergence of FedAvg on non-IID data. arXiv preprint arXiv:1907.02189 (2019).","journal-title":"arXiv preprint arXiv:1907.02189"},{"key":"e_1_3_8_30_2","article-title":"VARF: An incentive mechanism of cross-silo federated learning in MEC","author":"Li Ying","year":"2023","unstructured":"Ying Li, Xingwei Wang, Rongfei Zeng, Mingzhou Yang, Kexin Li, Min Huang, and Schahram Dustdar. 2023. VARF: An incentive mechanism of cross-silo federated learning in MEC. IEEE Internet of Things Journal (2023).","journal-title":"IEEE Internet of Things Journal"},{"key":"e_1_3_8_31_2","doi-asserted-by":"publisher","DOI":"10.1002\/int.22879"},{"key":"e_1_3_8_32_2","doi-asserted-by":"publisher","DOI":"10.1145\/3690648"},{"key":"e_1_3_8_33_2","first-page":"1273","volume-title":"Artificial Intelligence and Statistics","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 Artificial Intelligence and Statistics. PMLR, 1273\u20131282."},{"key":"e_1_3_8_34_2","doi-asserted-by":"publisher","DOI":"10.1145\/3458864.3467681"},{"key":"e_1_3_8_35_2","doi-asserted-by":"publisher","unstructured":"Reyes-Ortiz Jorge Anguita Davide Ghio Alessandro Oneto Luca and Xavier Parra. 2013. Human activity recognition using smartphones. UCI Machine Learning Repository. DOI:10.24432\/C54S4K","DOI":"10.24432\/C54S4K"},{"key":"e_1_3_8_36_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2019.2944481"},{"key":"e_1_3_8_37_2","doi-asserted-by":"publisher","DOI":"10.1109\/BigData47090.2019.9006280"},{"key":"e_1_3_8_38_2","article-title":"Personalized federated learning for heterogeneous edge device: Self-knowledge distillation approach","author":"Singh Neha","year":"2023","unstructured":"Neha Singh, Jatin Rupchandani, and Mainak Adhikari. 2023. Personalized federated learning for heterogeneous edge device: Self-knowledge distillation approach. IEEE Transactions on Consumer Electronics (2023).","journal-title":"IEEE Transactions on Consumer Electronics"},{"key":"e_1_3_8_39_2","doi-asserted-by":"publisher","DOI":"10.1145\/3595916.3626371"},{"key":"e_1_3_8_40_2","article-title":"Edge-based communication optimization for distributed federated learning","author":"Wang Tian","year":"2021","unstructured":"Tian Wang, Yan Liu, Xi Zheng, Hong-Ning Dai, Weijia Jia, and Mande Xie. 2021. Edge-based communication optimization for distributed federated learning. IEEE Transactions on Network Science and Engineering (2021).","journal-title":"IEEE Transactions on Network Science and Engineering"},{"key":"e_1_3_8_41_2","doi-asserted-by":"publisher","DOI":"10.1109\/CAMAD50429.2020.9209263"},{"key":"e_1_3_8_42_2","article-title":"Communication-efficient adaptive federated learning","author":"Wang Yujia","year":"2022","unstructured":"Yujia Wang, Lu Lin, and Jinghui Chen. 2022. Communication-efficient adaptive federated learning. arXiv preprint arXiv:2205.02719 (2022).","journal-title":"arXiv preprint arXiv:2205.02719"},{"key":"e_1_3_8_43_2","doi-asserted-by":"publisher","DOI":"10.24432\/C5HK59"},{"issue":"1","key":"e_1_3_8_44_2","first-page":"1","article-title":"Communication-efficient federated learning via knowledge distillation","volume":"13","author":"Wu Chuhan","year":"2022","unstructured":"Chuhan Wu, Fangzhao Wu, Lingjuan Lyu, Yongfeng Huang, and Xing Xie. 2022. Communication-efficient federated learning via knowledge distillation. Nature Communications 13, 1 (2022), 1\u20138.","journal-title":"Nature Communications"},{"key":"e_1_3_8_45_2","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2020.3031503"},{"key":"e_1_3_8_46_2","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2021.3131852"},{"key":"e_1_3_8_47_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3095915"},{"key":"e_1_3_8_48_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i8.20894"},{"key":"e_1_3_8_49_2","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2022.3211998"}],"container-title":["ACM Transactions on Internet Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3716870","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3716870","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:19:16Z","timestamp":1750295956000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3716870"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,25]]},"references-count":48,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,5,31]]}},"alternative-id":["10.1145\/3716870"],"URL":"https:\/\/doi.org\/10.1145\/3716870","relation":{},"ISSN":["1533-5399","1557-6051"],"issn-type":[{"value":"1533-5399","type":"print"},{"value":"1557-6051","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,25]]},"assertion":[{"value":"2024-09-25","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-02-01","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-04-25","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}