{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T01:14:05Z","timestamp":1773450845287,"version":"3.50.1"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"14","license":[{"start":{"date-parts":[[2023,4,18]],"date-time":"2023-04-18T00:00:00Z","timestamp":1681776000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,4,18]],"date-time":"2023-04-18T00:00:00Z","timestamp":1681776000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"published-print":{"date-parts":[[2023,9]]},"DOI":"10.1007\/s11227-023-05252-w","type":"journal-article","created":{"date-parts":[[2023,4,18]],"date-time":"2023-04-18T17:02:33Z","timestamp":1681837353000},"page":"15435-15458","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Reliable federated learning in a cloud-fog-IoT environment"],"prefix":"10.1007","volume":"79","author":[{"given":"Mradula","family":"Sharma","sequence":"first","affiliation":[]},{"given":"Parmeet","family":"Kaur","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,4,18]]},"reference":[{"key":"5252_CR1","doi-asserted-by":"publisher","first-page":"685","DOI":"10.1007\/s12525-021-00475-2","volume":"31","author":"C Janiesch","year":"2021","unstructured":"Janiesch C, Zschech P, Heinrich K (2021) Machine learning and deep learning. Electron Mark 31:685\u2013695. https:\/\/doi.org\/10.1007\/s12525-021-00475-2","journal-title":"Electron Mark"},{"issue":"7","key":"5252_CR2","doi-asserted-by":"publisher","first-page":"5476","DOI":"10.1109\/JIOT.2020.3030072","volume":"8","author":"S AbdulRahman","year":"2020","unstructured":"AbdulRahman S, Tout H, Ould-Slimane H, Mourad A, Talhi C, Guizani M (2020) A survey on federated learning: the journey from centralized to distributed on-site learning and beyond. IEEE Internet Things J 8(7):5476\u20135497","journal-title":"IEEE Internet Things J"},{"issue":"3","key":"5252_CR3","first-page":"1","volume":"13","author":"Q Yang","year":"2019","unstructured":"Yang Q, Liu Y, Cheng Y, Kang Y, Chen T, Yu H (2019) Federated learning. Synth Lect Artif Intell Mach Learn 13(3):1\u2013207","journal-title":"Synth Lect Artif Intell Mach Learn"},{"key":"5252_CR4","unstructured":"Hard A, Kanishka R, Rajiv M, Ramaswamy S, Beaufays F, Augenstein S, Eichner H, Kiddon C, Ramage D (2018) Federated learning for mobile keyboard prediction. arXiv preprint arXiv:1811.03604"},{"key":"5252_CR5","unstructured":"McMahan B, Moore E, Ramage D, Hampson S, Arcas BA (2017). Communication-efficient learning of deep networks from decentralized data. In:\u00a0Artificial intelligence and statistics. pp. 1273\u20131282. PMLR"},{"key":"5252_CR6","unstructured":"Sang N, Salcic Z, Zhang X (2018) Big data processing in fog-smart parking case study. In:\u00a02018 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Ubiquitous Computing & Communications, Big Data & Cloud Computing, Social Computing & Networking, Sustainable Computing & Communications (ISPA\/IUCC\/BDCloud\/SocialCom\/SustainCom), pp. 127\u2013134. IEEE, 2018"},{"key":"5252_CR7","unstructured":"Lalle Y, Fourati M, Fourati LC, Barraca JPA. Hierarchical clustering federated learning-based blockchain scheme for privacy-preserving in water demand prediction.\u00a0Available at SSRN 4108575"},{"key":"5252_CR8","doi-asserted-by":"publisher","DOI":"10.1007\/s40860-022-00185-2","author":"M Sharma","year":"2022","unstructured":"Sharma M, Kaur P (2022) XLAAM: explainable LSTM-based activity and anomaly monitoring in a fog environment. J Reliab Intell Environ. https:\/\/doi.org\/10.1007\/s40860-022-00185-2","journal-title":"J Reliab Intell Environ"},{"issue":"7","key":"5252_CR9","doi-asserted-by":"publisher","first-page":"6132","DOI":"10.1109\/JIOT.2019.2957314","volume":"7","author":"A Saleem","year":"2019","unstructured":"Saleem A, Khan A, Malik SUR, Pervaiz H, Malik H, Alam M, Jindal A (2019) FESDA: fog-enabled secure data aggregation in smart grid IoT network. IEEE Internet Things J 7(7):6132\u20136142","journal-title":"IEEE Internet Things J"},{"issue":"2","key":"5252_CR10","doi-asserted-by":"publisher","first-page":"107","DOI":"10.3233\/MGS-220211","volume":"18","author":"P Kaur","year":"2022","unstructured":"Kaur P (2022) Fault tolerant data offloading in opportunistic fog enhanced IoT architecture. Multiagent Grid Syst 18(2):107\u2013118","journal-title":"Multiagent Grid Syst"},{"issue":"1","key":"5252_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10723-021-09591-x","volume":"20","author":"K Dubey","year":"2022","unstructured":"Dubey K, Sharma SC, Kumar M (2022) A secure IoT applications allocation framework for integrated fog-cloud environment. J Grid Comput 20(1):1\u201323","journal-title":"J Grid Comput"},{"issue":"2","key":"5252_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3301443","volume":"19","author":"C Puliafito","year":"2019","unstructured":"Puliafito C, Mingozzi E, Longo F, Puliafito A, Rana O (2019) Fog computing for the internet of things: a survey. ACM Trans Internet Technol (TOIT) 19(2):1\u201341","journal-title":"ACM Trans Internet Technol (TOIT)"},{"key":"5252_CR13","unstructured":"Bonawitz K et al. (2019) Towards federated learning at scale: system design. Available: arXiv:1902.01046"},{"issue":"10","key":"5252_CR14","doi-asserted-by":"publisher","first-page":"8456","DOI":"10.1109\/JIOT.2020.3046509","volume":"8","author":"R Saha","year":"2020","unstructured":"Saha R, Misra S, Deb PK (2020) FogFL: fog-assisted federated learning for resource-constrained IoT devices. IEEE Internet Things J 8(10):8456\u20138463","journal-title":"IEEE Internet Things J"},{"key":"5252_CR15","doi-asserted-by":"crossref","unstructured":"Liu L, Zhang J, Song SH, Letaief KB (2020) Client-edge-cloud hierarchical federated learning. In:\u00a0ICC 2020\u20132020 IEEE International Conference on Communications (ICC), pp 1\u20136. IEEE, 2020","DOI":"10.1109\/ICC40277.2020.9148862"},{"key":"5252_CR16","unstructured":"Mathur A, Beutel DJ, de Gusm\u00e3o PPB, Fernandez-Marques J, Topal T, Qiu X, Parcollet T, Gao Y, Lane ND (2021) On-device federated learning with flower. arXiv preprint arXiv:2104.03042"},{"issue":"4","key":"5252_CR17","doi-asserted-by":"publisher","first-page":"573","DOI":"10.3390\/electronics11040573","volume":"11","author":"N LlisterriGim\u00e9nez","year":"2022","unstructured":"LlisterriGim\u00e9nez N, Grau MM, PueyoCentelles R, Freitag F (2022) On-device training of machine learning models on microcontrollers with federated learning. Electronics 11(4):573","journal-title":"Electronics"},{"issue":"2","key":"5252_CR18","doi-asserted-by":"publisher","first-page":"1136","DOI":"10.1109\/JIOT.2021.3078543","volume":"9","author":"C Li","year":"2021","unstructured":"Li C, Li G, Varshney PK (2021) Decentralized federated learning via mutual knowledge transfer. IEEE Internet Things J 9(2):1136\u20131147","journal-title":"IEEE Internet Things J"},{"key":"5252_CR19","doi-asserted-by":"crossref","unstructured":"Wang H, Wang L (2022) FedKG: Model-Optimized Federated Learning for Local Client Training with Non-IID Private Data. In:\u00a02021 Ninth International Conference on Advanced Cloud and Big Data (CBD). pp 51\u201357. IEEE","DOI":"10.1109\/CBD54617.2021.00018"},{"key":"5252_CR20","doi-asserted-by":"crossref","unstructured":"Huang W, Ye M, Du B (2022) Learn from others and be yourself in heterogeneous federated learning. In\u00a0Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 10143\u201310153","DOI":"10.1109\/CVPR52688.2022.00990"},{"key":"5252_CR21","unstructured":"Charles Z, Konecny J (2021) Convergence and accuracy trade-offs in federated learning ` and meta-learning. In International Conference on Artificial Intelligence and Statistics, pages 2575\u20132583. PMLR, 2021"},{"key":"5252_CR22","unstructured":"Li T, Sahu AK, Zaheer M, Sanjabi M, Talwalkar A, Smith V (2018) On the convergence of federated optimization in heterogeneous networks. arXiv preprint arXiv:1812.06127"},{"key":"5252_CR23","unstructured":"Karimireddy SP, Kale S, Mohri M, Reddi S, Stich S, Suresh AT (2020) Scaffold: Stochastic controlled averaging for federated learning. In International Conference on Machine Learning. pp 5132\u20135143"},{"key":"5252_CR24","unstructured":"Pathak R, Wainwright MJ (2020) Fedsplit: an algorithmic framework for fast federated optimization. arXiv preprint arXiv:2005.05238"},{"key":"5252_CR25","first-page":"7611","volume":"33","author":"J Wang","year":"2020","unstructured":"Wang J, Liu Q, Liang H, Joshi G, Vincent Poor H (2020) Tackling the objective inconsistency problem in heterogeneous federated optimization. Adv Neural Inf Process Syst 33:7611\u20137623","journal-title":"Adv Neural Inf Process Syst"},{"key":"5252_CR26","first-page":"14606","volume":"34","author":"A Mitra","year":"2021","unstructured":"Mitra A, Jaafar R, Pappas GJ, Hassani H (2021) Linear convergence in federated learning: tackling client heterogeneity and sparse gradients. Adv Neural Inf Process Syst 34:14606\u201314619","journal-title":"Adv Neural Inf Process Syst"},{"issue":"6","key":"5252_CR27","doi-asserted-by":"publisher","first-page":"1094","DOI":"10.1016\/j.dcan.2022.03.013","volume":"8","author":"C Bay\u0131lm\u0131\u015f","year":"2022","unstructured":"Bay\u0131lm\u0131\u015f C, Ali Ebleme M, \u00c7avu\u015fo\u011flu \u00dc, K\u00fc\u00e7\u00fck K, Sevin A (2022) A survey on communication protocols and performance evaluations for Internet of Things. Digit Commun Netw 8(6):1094\u20131104","journal-title":"Digit Commun Netw"},{"key":"5252_CR28","doi-asserted-by":"crossref","unstructured":"Mu B, Bakiras S (2013) Private proximity detection for convex polygons. In: Proceedings of the 12th International ACM Workshop on Data Engineering for Wireless and Mobile Acess. pp. 36\u201343","DOI":"10.1145\/2486084.2486087"},{"key":"5252_CR29","unstructured":"Bonawitz K, Eichner H, Grieskamp W, Huba D, Ingerman A, Ivanov V, Kiddon C et al. (2019) Towards federated learning at scale: System design.In:\u00a0Proceedings of Machine Learning and Systems,\u00a01: 374\u2013388"},{"issue":"9","key":"5252_CR30","doi-asserted-by":"publisher","first-page":"9905","DOI":"10.1007\/s11227-021-03672-0","volume":"77","author":"P Pereira","year":"2021","unstructured":"Pereira P, Araujo J, Melo C, Santos V, Maciel P (2021) Analytical models for availability evaluation of edge and fog computing nodes. J Supercomput 77(9):9905\u20139933","journal-title":"J Supercomput"},{"key":"5252_CR31","unstructured":"Sharareh A, Futuhi E, Karimi S (2020) On distributed algorithms for minimum dominating set problem, from theory to application.\u00a0arXiv preprint arXiv:2012.04883"},{"issue":"6","key":"5252_CR32","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1109\/MSP.2012.2211477","volume":"29","author":"L Deng","year":"2012","unstructured":"Deng L (2012) The mnist database of handwritten digit images for machine learning research. IEEE Signal Process Mag 29(6):141\u2013142","journal-title":"IEEE Signal Process Mag"},{"key":"5252_CR33","unstructured":"Shoham N, Avidor T, Keren A, Israel N, Benditkis D, Mor-Yosef L, Zeitak I (2019) Overcoming forgetting in federated learning on non-iid data.\u00a0arXiv preprint arXiv:1910.07796"},{"issue":"12","key":"5252_CR34","doi-asserted-by":"publisher","first-page":"14356","DOI":"10.1007\/s11227-021-03849-7","volume":"77","author":"I Westby","year":"2021","unstructured":"Westby I, Yang X, Liu T, Xu H (2021) FPGA acceleration on a multi-layer perceptron neural network for digit recognition. J Supercomput 77(12):14356\u201314373","journal-title":"J Supercomput"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-023-05252-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-023-05252-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-023-05252-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,14]],"date-time":"2023-08-14T10:24:16Z","timestamp":1692008656000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-023-05252-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,18]]},"references-count":34,"journal-issue":{"issue":"14","published-print":{"date-parts":[[2023,9]]}},"alternative-id":["5252"],"URL":"https:\/\/doi.org\/10.1007\/s11227-023-05252-w","relation":{},"ISSN":["0920-8542","1573-0484"],"issn-type":[{"value":"0920-8542","type":"print"},{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,18]]},"assertion":[{"value":"2 April 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 April 2023","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interest with respect to this manuscript.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"No ethical approval was required for this research.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}