{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T09:57:25Z","timestamp":1785578245578,"version":"3.56.0"},"reference-count":30,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T00:00:00Z","timestamp":1779321600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62302095"],"award-info":[{"award-number":["62302095"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82501450"],"award-info":[{"award-number":["82501450"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Intelligent Systems with Applications"],"published-print":{"date-parts":[[2026,9]]},"DOI":"10.1016\/j.iswa.2026.200678","type":"journal-article","created":{"date-parts":[[2026,5,26]],"date-time":"2026-05-26T23:51:10Z","timestamp":1779839470000},"page":"200678","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["FedRTAS: Communication-efficient over-the-air federated learning with Replay-Based Training and Trend-Aware Stopping"],"prefix":"10.1016","volume":"31","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-7937-6720","authenticated-orcid":false,"given":"An","family":"Gao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3405-5942","authenticated-orcid":false,"given":"Shuang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-8877-6451","authenticated-orcid":false,"given":"Xiao","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.iswa.2026.200678_b1","series-title":"2019 IEEE international symposium on information theory","first-page":"1432","article-title":"Machine learning at the wireless edge: Distributed stochastic gradient descent Over-the-Air","author":"Amiri","year":"2019"},{"key":"10.1016\/j.iswa.2026.200678_b2","series-title":"Advances in neural information processing systems","first-page":"402","article-title":"Overfitting in neural nets: Backpropagation, conjugate gradient, and early stopping","volume":"vol. 13","author":"Caruana","year":"2000"},{"key":"10.1016\/j.iswa.2026.200678_b3","series-title":"NOMS 2025-2025 IEEE network operations and management symposium","first-page":"01","article-title":"Federated learning with MMD-based early stopping for adaptive GNSS interference classification","author":"Gaikwad","year":"2025"},{"issue":"9","key":"10.1016\/j.iswa.2026.200678_b4","doi-asserted-by":"crossref","first-page":"8919","DOI":"10.1109\/TITS.2023.3275741","article-title":"Edge intelligence in intelligent transportation systems: A survey","volume":"24","author":"Gong","year":"2023","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"key":"10.1016\/j.iswa.2026.200678_b5","doi-asserted-by":"crossref","first-page":"81004","DOI":"10.1109\/ACCESS.2024.3410026","article-title":"FedArtML: A tool to facilitate the generation of Non-IID datasets in a controlled way to support federated learning research","volume":"12","author":"Gutierrez","year":"2024","journal-title":"IEEE Access"},{"key":"10.1016\/j.iswa.2026.200678_b6","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2026.133186","article-title":"Strengthen the weak, align the strong: A federated enhanced iterative learning framework for cross-machine fault diagnosis","volume":"678","author":"Hu","year":"2026","journal-title":"Neurocomputing"},{"issue":"1","key":"10.1016\/j.iswa.2026.200678_b7","doi-asserted-by":"crossref","first-page":"27988","DOI":"10.1038\/srep27988","article-title":"Multi-class texture analysis in colorectal cancer histology","volume":"6","author":"Kather","year":"2016","journal-title":"Scientific Reports"},{"issue":"7","key":"10.1016\/j.iswa.2026.200678_b8","doi-asserted-by":"crossref","first-page":"5489","DOI":"10.1109\/TPAMI.2025.3551732","article-title":"Re-Fed+: A better replay strategy for federated incremental learning","volume":"47","author":"Li","year":"2025","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"4","key":"10.1016\/j.iswa.2026.200678_b9","doi-asserted-by":"crossref","first-page":"3347","DOI":"10.1109\/TKDE.2021.3124599","article-title":"A survey on federated learning systems: Vision, hype and reality for data privacy and protection","volume":"35","author":"Li","year":"2023","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"issue":"11","key":"10.1016\/j.iswa.2026.200678_b10","doi-asserted-by":"crossref","first-page":"1879","DOI":"10.1109\/TPDS.2024.3436874","article-title":"SR-FDIL: Synergistic replay for federated domain-incremental learning","volume":"35","author":"Li","year":"2024","journal-title":"IEEE Transactions on Parallel and Distributed Systems"},{"issue":"7","key":"10.1016\/j.iswa.2026.200678_b11","doi-asserted-by":"crossref","first-page":"1549","DOI":"10.1109\/LCOMM.2025.3567387","article-title":"Over-the-Air fair federated learning via multi-objective optimization","volume":"29","author":"Mohajer Hamidi","year":"2025","journal-title":"IEEE Communications Letters"},{"key":"10.1016\/j.iswa.2026.200678_b12","series-title":"ICC 2024 - IEEE international conference on communications","first-page":"5565","article-title":"An Autoencoder-Based constellation design for AirComp in wireless federated learning","author":"Mu","year":"2024"},{"issue":"3","key":"10.1016\/j.iswa.2026.200678_b13","doi-asserted-by":"crossref","first-page":"1622","DOI":"10.1109\/COMST.2021.3075439","article-title":"Federated learning for internet of things: A comprehensive survey","volume":"23","author":"Nguyen","year":"2021","journal-title":"IEEE Communications Surveys & Tutorials"},{"issue":"12","key":"10.1016\/j.iswa.2026.200678_b14","doi-asserted-by":"crossref","first-page":"14514","DOI":"10.1109\/TMC.2024.3447000","article-title":"Flrce: Resource-efficient federated learning with early-stopping strategy","volume":"23","author":"Niu","year":"2024","journal-title":"IEEE Transactions on Mobile Computing"},{"key":"10.1016\/j.iswa.2026.200678_b15","series-title":"2024 IEEE 4th international conference on ICT in business industry & government","first-page":"1","article-title":"Differential privacy in federated learning using noise multipliers: An analysis on MNIST dataset","author":"Panchal","year":"2024"},{"key":"10.1016\/j.iswa.2026.200678_b16","doi-asserted-by":"crossref","DOI":"10.1016\/j.cviu.2023.103882","article-title":"FedER: Federated learning through experience replay and privacy-preserving data synthesis","volume":"238","author":"Pennisi","year":"2024","journal-title":"Computer Vision and Image Understanding"},{"key":"10.1016\/j.iswa.2026.200678_b17","series-title":"Neural networks: tricks of the trade","first-page":"55","article-title":"Early Stopping \u2013 But when?","volume":"vol. 1524","author":"Prechelt","year":"1998"},{"key":"10.1016\/j.iswa.2026.200678_b18","series-title":"Computational and experimental simulations in engineering","first-page":"905","article-title":"DPFL-AES: Differential privacy federated learning based on adam early stopping","author":"Qian","year":"2024"},{"issue":"11","key":"10.1016\/j.iswa.2026.200678_b19","doi-asserted-by":"crossref","first-page":"3078","DOI":"10.1109\/JSAC.2024.3431572","article-title":"Massive digital Over-the-Air computation for communication-efficient federated edge learning","volume":"42","author":"Qiao","year":"2024","journal-title":"IEEE Journal on Selected Areas in Communications"},{"key":"10.1016\/j.iswa.2026.200678_b20","series-title":"2024 IEEE 30th international conference on parallel and distributed systems","first-page":"326","article-title":"Optimal power control for Over-the-Air federated learning with gradient compression","author":"Ruan","year":"2024"},{"key":"10.1016\/j.iswa.2026.200678_b21","doi-asserted-by":"crossref","first-page":"701","DOI":"10.1016\/j.future.2024.07.017","article-title":"Digital twin and federated learning enabled cyberthreat detection system for IoT networks","volume":"161","author":"Salim","year":"2024","journal-title":"Future Generation Computer Systems"},{"key":"10.1016\/j.iswa.2026.200678_b22","series-title":"2024 IEEE international conference on acoustics, speech, and signal processing workshops","first-page":"429","article-title":"Over the air federated learning in the presence of impulsive noise","author":"Shaban","year":"2024"},{"issue":"21","key":"10.1016\/j.iswa.2026.200678_b23","doi-asserted-by":"crossref","first-page":"34567","DOI":"10.1109\/JIOT.2024.3372518","article-title":"A survey of IoT privacy security: Architecture, technology, challenges, and trends","volume":"11","author":"Sun","year":"2024","journal-title":"IEEE Internet of Things Journal"},{"issue":"10","key":"10.1016\/j.iswa.2026.200678_b24","doi-asserted-by":"crossref","first-page":"9368","DOI":"10.1109\/TMC.2024.3361876","article-title":"FedCache: A knowledge Cache-Driven federated learning architecture for personalized edge intelligence","volume":"23","author":"Wu","year":"2024","journal-title":"IEEE Transactions on Mobile Computing"},{"issue":"3","key":"10.1016\/j.iswa.2026.200678_b25","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1109\/TPDS.2024.3349617","article-title":"EcoFed: Efficient communication for DNN partitioning-based federated learning","volume":"35","author":"Wu","year":"2024","journal-title":"IEEE Transactions on Parallel and Distributed Systems"},{"issue":"11","key":"10.1016\/j.iswa.2026.200678_b26","doi-asserted-by":"crossref","first-page":"1778","DOI":"10.1109\/JPROC.2021.3119950","article-title":"Edge intelligence: Empowering intelligence to the edge of network","volume":"109","author":"Xu","year":"2021","journal-title":"Proceedings of the IEEE"},{"issue":"6","key":"10.1016\/j.iswa.2026.200678_b27","doi-asserted-by":"crossref","DOI":"10.3390\/fi15060209","article-title":"A DQN-Based multi-objective participant selection for efficient federated learning","volume":"15","author":"Xu","year":"2023","journal-title":"Future Internet"},{"issue":"1","key":"10.1016\/j.iswa.2026.200678_b28","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1038\/s41597-022-01721-8","article-title":"MedMNIST v2 \u2013 A large-scale lightweight benchmark for 2D and 3D biomedical image classification","volume":"10","author":"Yang","year":"2023","journal-title":"Scientific Data"},{"issue":"2","key":"10.1016\/j.iswa.2026.200678_b29","doi-asserted-by":"crossref","first-page":"1884","DOI":"10.1109\/TII.2022.3183465","article-title":"Optimizing federated learning with deep reinforcement learning for digital twin empowered industrial IoT","volume":"19","author":"Yang","year":"2023","journal-title":"IEEE Transactions on Industrial Informatics"},{"key":"10.1016\/j.iswa.2026.200678_b30","series-title":"Advances in neural information processing systems","first-page":"21285","article-title":"Towards theoretically understanding why sgd generalizes better than Adam in deep learning","volume":"vol. 33","author":"Zhou","year":"2020"}],"container-title":["Intelligent Systems with Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2667305326000530?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2667305326000530?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T08:59:28Z","timestamp":1785574768000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2667305326000530"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":30,"alternative-id":["S2667305326000530"],"URL":"https:\/\/doi.org\/10.1016\/j.iswa.2026.200678","relation":{},"ISSN":["2667-3053"],"issn-type":[{"value":"2667-3053","type":"print"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"FedRTAS: Communication-efficient over-the-air federated learning with Replay-Based Training and Trend-Aware Stopping","name":"articletitle","label":"Article Title"},{"value":"Intelligent Systems with Applications","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.iswa.2026.200678","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Authors. Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"200678"}}