{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,2]],"date-time":"2025-07-02T02:16:54Z","timestamp":1751422614113,"version":"3.37.3"},"reference-count":51,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,7,1]],"date-time":"2021-07-01T00:00:00Z","timestamp":1625097600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,7,1]],"date-time":"2021-07-01T00:00:00Z","timestamp":1625097600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100008530","name":"European Regional Development Fund","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100008530","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100008530","name":"European Regional Development Fund","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100008530","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,7]]},"DOI":"10.1109\/compsac51774.2021.00274","type":"proceedings-article","created":{"date-parts":[[2021,9,9]],"date-time":"2021-09-09T20:49:27Z","timestamp":1631220567000},"page":"1819-1824","source":"Crossref","is-referenced-by-count":11,"title":["Dew Intelligence: Federated learning perspective"],"prefix":"10.1109","author":[{"given":"Emanuel","family":"Guberovi\u0107","sequence":"first","affiliation":[{"name":"University of Zagreb,Faculty of Electrical Engineering and Computing,Zagreb,Croatia"}]},{"given":"Tomislav","family":"Lipi\u0107","sequence":"additional","affiliation":[{"name":"Ru&#x0111;er Bo&#x0161;kovi&#x0107; Institute,Laboratory for Machine Learning and Knowledge Representation,Zagreb,Croatia"}]},{"given":"Igor","family":"\u010cavrak","sequence":"additional","affiliation":[{"name":"University of Zagreb,Faculty of Electrical Engineering and Computing,Zagreb,Croatia"}]}],"member":"263","reference":[{"article-title":"Federated multitask learning","year":"2017","author":"smith","key":"ref39"},{"article-title":"Federated learning: Strategies for improving communication efficiency","year":"2016","author":"kone?n?","key":"ref38"},{"key":"ref33","article-title":"Federated multi-task learning","volume":"30","author":"smith","year":"2017","journal-title":"Advances in neural information processing systems"},{"article-title":"Crypten","year":"2021","author":"team","key":"ref32"},{"key":"ref31","first-page":"8024","article-title":"Pytorch: An imperative style, high-performance deep learning library","author":"paszke","year":"2019","journal-title":"Advances in Neural IInformation Processing Systems"},{"article-title":"A generic framework for privacy preserving deep learning","year":"2018","author":"ryffel","key":"ref30"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1145\/2810103.2813677"},{"article-title":"Practical secure aggregation for federated learning on user-held data","year":"2016","author":"bonawitz","key":"ref36"},{"article-title":"Leaf: A benchmark for federated settings","year":"2019","author":"caldas","key":"ref35"},{"article-title":"Fate (federated ai technology enabler)","year":"2021","author":"department","key":"ref34"},{"year":"2021","key":"ref28","article-title":"Tensorflow federated: Machine learning on decentralized data"},{"key":"ref27","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","author":"mcmahan","year":"2017","journal-title":"Artificial Intelligence and Statistics"},{"article-title":"TensorFlow: Large-scale machine learning on heterogeneous systems","year":"2015","author":"abadi","key":"ref29"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.23919\/TST.2017.8195353"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1145\/1721654.1721672"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.001.2000410"},{"article-title":"Machine learning systems for highly-distributed and rapidly-growing data","year":"2019","author":"hsieh","key":"ref22"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1155\/2018\/8738613"},{"article-title":"One weird trick for parallelizing convolutional neural networks","year":"2014","author":"krizhevsky","key":"ref24"},{"article-title":"Unifying data, model and hybrid parallelism in deep learning via tensor tiling","year":"2018","author":"wang","key":"ref23"},{"article-title":"Federated learning of deep networks using model averaging","year":"2016","author":"mcmahan","key":"ref26"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1145\/2640087.2644155"},{"article-title":"Federated learning for time series forecasting using lstm networks: Exploiting similarities through clustering","year":"2019","author":"d\u00edaz gonz\u00e1lez","key":"ref50"},{"journal-title":"Building Machine Learning Pipelines","year":"2020","author":"hapke","key":"ref51"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2017.2775042"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1145\/3298981"},{"key":"ref40","first-page":"19","article-title":"Towards taming the resource and data heterogeneity in federated learning","author":"chai","year":"2019","journal-title":"2019 USENIX Conference on Operational Machine Learning (OpML 19)"},{"key":"ref12","doi-asserted-by":"crossref","first-page":"5476","DOI":"10.1109\/JIOT.2020.3030072","article-title":"A survey on federated learning: The journey from centralized to distributed on-site learning and beyond","volume":"8","author":"abdulrahman","year":"2021","journal-title":"IEEE Internet of Things Journal"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2019.103291"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2020.2986024"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2020.2975749"},{"article-title":"A survey on federated learning systems: vision, hype and reality for data privacy and protection","year":"2019","author":"li","key":"ref16"},{"article-title":"No peek: A survey of private distributed deep learning","year":"2018","author":"vepakomma","key":"ref17"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1145\/3377454"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2020.2970550"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1177\/1550147719867072"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2019.2904897"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1504\/IJCC.2015.071717"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2020.2970550"},{"key":"ref8","first-page":"1","article-title":"Definition and categorization of dew computing","volume":"3","author":"wang","year":"2016","journal-title":"Open Journal of Cloud Computing"},{"key":"ref7","first-page":"16","article-title":"Scalable distributed computing hierarchy: Cloud, fog and dew computing","volume":"2","author":"skala","year":"2015","journal-title":"Open Journal of Cloud Computing (OJCC)"},{"key":"ref49","article-title":"Distributed fine-tuning of language models on private data","author":"popov","year":"2018","journal-title":"International Conference on Learning Representations"},{"key":"ref9","doi-asserted-by":"crossref","first-page":"100186","DOI":"10.1016\/j.iot.2020.100186","article-title":"Dew Computing Architecture for Cyber-Physical Systems and IoT","volume":"11","author":"gushev","year":"2020","journal-title":"Internet of Things"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/MIS.2020.2988604"},{"article-title":"A preliminary opinion on data protection and scientific research","year":"2020","author":"supervisor","key":"ref45"},{"article-title":"Split learning for health: Distributed deep learning without sharing raw patient data","year":"2018","author":"vepakomma","key":"ref48"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2018.01.007"},{"article-title":"Federated learning for mobile keyboard prediction","year":"2018","author":"hard","key":"ref42"},{"article-title":"Federated learning with non-iid data","year":"2018","author":"zhao","key":"ref41"},{"key":"ref44","article-title":"Beyond the hipaa privacy rule: enhancing privacy, improving health through research","author":"gostin","year":"2009","journal-title":"The National Academies Collection Reports funded by National Institutes of Health"},{"article-title":"Federated learning for emoji prediction in a mobile keyboard","year":"2019","author":"ramaswamy","key":"ref43"}],"event":{"name":"2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC)","start":{"date-parts":[[2021,7,12]]},"location":"Madrid, Spain","end":{"date-parts":[[2021,7,16]]}},"container-title":["2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9529349\/9529356\/09529852.pdf?arnumber=9529852","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,15]],"date-time":"2024-01-15T21:02:04Z","timestamp":1705352524000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9529852\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7]]},"references-count":51,"URL":"https:\/\/doi.org\/10.1109\/compsac51774.2021.00274","relation":{},"subject":[],"published":{"date-parts":[[2021,7]]}}}