{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T08:54:42Z","timestamp":1730278482998,"version":"3.28.0"},"reference-count":23,"publisher":"IEEE","license":[{"start":{"date-parts":[[2022,11,1]],"date-time":"2022-11-01T00:00:00Z","timestamp":1667260800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,11,1]],"date-time":"2022-11-01T00:00:00Z","timestamp":1667260800000},"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":[[2022,11,1]]},"DOI":"10.1109\/itw54588.2022.9965838","type":"proceedings-article","created":{"date-parts":[[2022,12,7]],"date-time":"2022-12-07T20:47:00Z","timestamp":1670446020000},"page":"446-451","source":"Crossref","is-referenced-by-count":4,"title":["DiPLe: Learning Directed Collaboration Graphs for Peer-to-Peer Personalized Learning"],"prefix":"10.1109","author":[{"given":"Xue","family":"Zheng","sequence":"first","affiliation":[{"name":"The Ohio State University,Electrical and Computer Engineering"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Parinaz","family":"Naghizadeh","sequence":"additional","affiliation":[{"name":"The Ohio State University,Integrated Systems Engineering"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aylin","family":"Yener","sequence":"additional","affiliation":[{"name":"The Ohio State University,Electrical and Computer Engineering"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"article-title":"Adaptive personalized federated learning","year":"2020","author":"deng","key":"ref10"},{"article-title":"Federated learning of a mixture of global and local models","year":"2020","author":"hanzely","key":"ref11"},{"article-title":"Three approaches for personalization with applications to federated learning","year":"2020","author":"mansour","key":"ref12"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.3015958"},{"key":"ref14","first-page":"19586","article-title":"An efficient framework for clustered federated learning","volume":"33","author":"ghosh","year":"2020","journal-title":"Advances in neural information processing systems"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/ISIT50566.2022.9834545"},{"article-title":"Personalized federated learning with first order model optimization","year":"2020","author":"zhang","key":"ref16"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/ITW54588.2022.9965838"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2017.7966217"},{"article-title":"Learning multiple layers of features from tiny images","year":"2009","author":"krizhevsky","key":"ref19"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2018.2859596"},{"article-title":"Federated learning using peer-to-peer network for decentralized orchestration of model weights","year":"2021","author":"behera","key":"ref3"},{"key":"ref6","first-page":"864","article-title":"Fully decentralized joint learning of personalized models and collaboration graphs","author":"zantedeschi","year":"2020","journal-title":"International Conference on Artificial Intelligence and Statistics"},{"key":"ref5","first-page":"509","article-title":"Decentralized collaborative learning of personalized models over networks","author":"vanhaesebrouck","year":"2017","journal-title":"Artificial Intelligence and Statistics"},{"article-title":"Personalized federated learning: A meta-learning approach","year":"2020","author":"fallah","key":"ref8"},{"article-title":"Federated multitask learning","year":"2017","author":"smith","key":"ref7"},{"article-title":"Can decentralized algorithms outperform centralized algorithms? a case study for decentralized parallel stochastic gradient descent","year":"2017","author":"lian","key":"ref2"},{"article-title":"Advances and open problems in federated learning","year":"2019","author":"kairouz","key":"ref1"},{"article-title":"Federated learning using a mixture of experts","year":"2020","author":"zec","key":"ref9"},{"key":"ref20","first-page":"21394","article-title":"Personalized federated learning with Moreau envelopes","volume":"33","author":"dinh","year":"2020","journal-title":"Advances in neural information processing systems"},{"article-title":"On the convergence of fedavg on non-iid data","year":"2019","author":"li","key":"ref22"},{"key":"ref21","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","author":"mcmahan","year":"2017","journal-title":"Artificial Intelligence and Statistics"},{"key":"ref23","first-page":"4615","article-title":"Agnostic federated learning","author":"mohri","year":"2019","journal-title":"International Conference on Machine Learning"}],"event":{"name":"2022 IEEE Information Theory Workshop (ITW)","start":{"date-parts":[[2022,11,1]]},"location":"Mumbai, India","end":{"date-parts":[[2022,11,9]]}},"container-title":["2022 IEEE Information Theory Workshop (ITW)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9965754\/9965755\/09965838.pdf?arnumber=9965838","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,26]],"date-time":"2022-12-26T19:42:21Z","timestamp":1672083741000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9965838\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,1]]},"references-count":23,"URL":"https:\/\/doi.org\/10.1109\/itw54588.2022.9965838","relation":{},"subject":[],"published":{"date-parts":[[2022,11,1]]}}}