{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,15]],"date-time":"2025-11-15T10:36:01Z","timestamp":1763202961908,"version":"3.29.0"},"reference-count":32,"publisher":"IEEE","license":[{"start":{"date-parts":[[2024,6,26]],"date-time":"2024-06-26T00:00:00Z","timestamp":1719360000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,6,26]],"date-time":"2024-06-26T00:00:00Z","timestamp":1719360000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,6,26]]},"DOI":"10.1109\/iscc61673.2024.10733670","type":"proceedings-article","created":{"date-parts":[[2024,10,31]],"date-time":"2024-10-31T17:32:28Z","timestamp":1730395948000},"page":"1-6","source":"Crossref","is-referenced-by-count":1,"title":["FLOWS: Federated Learning Optimization With Sinkhorn"],"prefix":"10.1109","author":[{"given":"Diletta","family":"Chiaro","sequence":"first","affiliation":[{"name":"University of Naples Federico II,Dept. of Mathematics and Applications \"R. Caccioppoli\",Naples,Italy"}]},{"given":"Fabio","family":"Giampaolo","sequence":"additional","affiliation":[{"name":"University of Naples Federico II,Dept. of Mathematics and Applications \"R. Caccioppoli\",Naples,Italy"}]},{"given":"Sara","family":"Amitrano","sequence":"additional","affiliation":[{"name":"University of Naples Federico II,Dept. of Mathematics and Applications \"R. Caccioppoli\",Naples,Italy"}]},{"given":"Francesco","family":"Piccialli","sequence":"additional","affiliation":[{"name":"University of Naples Federico II,Dept. of Mathematics and Applications \"R. Caccioppoli\",Naples,Italy"}]}],"member":"263","reference":[{"key":"ref1","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume-title":"Artificial intelligence and statistics.","author":"McMahan","year":"2017"},{"key":"ref2","first-page":"102402","article-title":"Privacy preservation in federated learning: An insightful survey from the gdpr perspective","volume-title":"Comput. Secur.","volume":"110","author":"Truong","year":"2020"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/MWC.004.2200381"},{"key":"ref4","first-page":"2423","article-title":"Multi-institutional collaborations for improving deep learning-based magnetic resonance image reconstruction using federated learning","volume-title":"2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Guo"},{"key":"ref5","first-page":"4734","article-title":"Federated learning with blockchain for autonomous vehicles: Analysis and design challenges","volume-title":"IEEE Transactions on Communications","volume":"68","author":"Pokhrel","year":"2020"},{"key":"ref6","first-page":"101890","article-title":"Fl-fd: Federated learning-based fall detection with multimodal data fusion","volume-title":"Inf. Fusion","volume":"99","author":"Qi","year":"2023"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2024.120217"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TCSS.2023.3259431"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1145\/3298981"},{"key":"ref10","first-page":"965","article-title":"Federated learning on non-iid data silos: An experimental study","volume-title":"2022 IEEE 38th International Conference on Data Engineering (ICDE)","author":"Li"},{"key":"ref11","article-title":"Federated learning with non-iid data","volume-title":"ArXiv","volume":"abs\/1806.00582","author":"Zhao","year":"2018"},{"key":"ref12","article-title":"Federated learning on non-iid data: A survey","volume-title":"ArXiv","volume":"abs\/2106.06843","author":"Zhu","year":"2021"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2023.101836"},{"key":"ref14","article-title":"Federated optimization in heterogeneous networks","volume-title":"Learning","author":"Sahu","year":"2018"},{"article-title":"Scaffold: Stochastic controlled averaging for federated learning","volume-title":"International Conference on Machine Learning","author":"Karimireddy","key":"ref15"},{"key":"ref16","doi-asserted-by":"crossref","first-page":"10 708","DOI":"10.1109\/CVPR46437.2021.01057","article-title":"Model-contrastive federated learning","volume-title":"2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Li","year":"2021"},{"key":"ref17","article-title":"Federated learning based on dynamic regularization","volume-title":"ArXiv","volume":"abs\/2111.04263","author":"Acar","year":"2021"},{"key":"ref18","first-page":"99","article-title":"The earth mover\u2019s distance as a metric for image retrieval","volume-title":"International Journal of Computer Vision","volume":"40","author":"Rubner","year":"2000"},{"key":"ref19","first-page":"343","article-title":"Concerning nonnegative matrices and doubly stochastic matrices","volume-title":"Pacific Journal of Mathematics","volume":"21","author":"Sinkhorn","year":"1967"},{"key":"ref20","article-title":"On the convergence of fedavg on non-iid data","volume-title":"ArXiv","volume":"abs\/1907.02189","author":"Li","year":"2019"},{"key":"ref21","first-page":"244","article-title":"A state-of-the-art survey on solving non-iid data in federated learning","volume-title":"Future Gener. Comput. Syst.","volume":"135","author":"Ma","year":"2022"},{"key":"ref22","first-page":"272","article-title":"Model aggregation techniques in federated learning: A comprehensive survey","volume-title":"Future Gener. Comput. Syst.","volume":"150","author":"Qi","year":"2023"},{"key":"ref23","article-title":"Tackling the objective inconsistency problem in heterogeneous federated optimization","volume-title":"ArXiv","volume":"abs\/2007.07481","author":"Wang","year":"2020"},{"key":"ref24","article-title":"Measuring the effects of nonidentical data distribution for federated visual classification","volume-title":"ArXiv","volume":"abs\/1909.06335","author":"Hsu","year":"2019"},{"key":"ref25","article-title":"Personalized federated learning with first order model optimization","volume-title":"ArXiv","volume":"abs\/2012.08565","author":"Zhang","year":"2020"},{"key":"ref26","article-title":"Robust federated learning: The case of affine distribution shifts","volume-title":"ArXiv","volume":"abs\/2006.08907","author":"Reisizadeh","year":"2020"},{"key":"ref27","first-page":"8387","article-title":"Local learning matters: Rethinking data heterogeneity in federated learning","volume-title":"2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Mendieta"},{"key":"ref28","article-title":"Differential properties of sinkhorn approximation for learning with wasserstein distance","volume-title":"Neural Information Processing Systems","author":"Luise","year":"2018"},{"article-title":"Optimal transport: Old and new","year":"2008","author":"Villani","key":"ref29"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2012.2211477"},{"journal-title":"Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms","year":"2017","author":"Xiao","key":"ref31"},{"article-title":"Learning multiple layers of features from tiny images","year":"2009","author":"Krizhevsky","key":"ref32"}],"event":{"name":"2024 IEEE Symposium on Computers and Communications (ISCC)","start":{"date-parts":[[2024,6,26]]},"location":"Paris, France","end":{"date-parts":[[2024,6,29]]}},"container-title":["2024 IEEE Symposium on Computers and Communications (ISCC)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/10733347\/10733557\/10733670.pdf?arnumber=10733670","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T05:09:51Z","timestamp":1732684191000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10733670\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,26]]},"references-count":32,"URL":"https:\/\/doi.org\/10.1109\/iscc61673.2024.10733670","relation":{},"subject":[],"published":{"date-parts":[[2024,6,26]]}}}