{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T05:35:29Z","timestamp":1730266529639,"version":"3.28.0"},"reference-count":28,"publisher":"IEEE","license":[{"start":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T00:00:00Z","timestamp":1658102400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T00:00:00Z","timestamp":1658102400000},"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":[[2022,7,18]]},"DOI":"10.1109\/ijcnn55064.2022.9892137","type":"proceedings-article","created":{"date-parts":[[2022,9,30]],"date-time":"2022-09-30T19:56:04Z","timestamp":1664567764000},"page":"1-8","source":"Crossref","is-referenced-by-count":0,"title":["BS-pFL: Enabling Low-Cost Personalized Federated Learning by Exploring Weight Gradient Sparsity"],"prefix":"10.1109","author":[{"given":"Lening","family":"Wang","sequence":"first","affiliation":[{"name":"University of Houston,ECE Department,Houston,USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Manojna","family":"Sistla","sequence":"additional","affiliation":[{"name":"University of Houston,ECE Department,Houston,USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingsong","family":"Chen","sequence":"additional","affiliation":[{"name":"East China Normal University,Department of Embedded Software and Systems,Shanghai,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Fu","sequence":"additional","affiliation":[{"name":"University of Houston,ECE Department,Houston,USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref10","article-title":"Deep compression: Compressing deep neural network with pruning, trained quantization and huffman coding","author":"han","year":"2016","journal-title":"4th International Conference on Learning Representations ICLR 2016 San Juan Puerto Rico May 2–4 2016 Conference Track Proceedings"},{"key":"ref11","article-title":"Federated learning of a mixture of global and local models","author":"hanzely","year":"2020","journal-title":"CoRR vol abs\/2002 05516"},{"journal-title":"Channel pruning for accelerating very deep neural networks","year":"2017","author":"he","key":"ref12"},{"journal-title":"Meta-SGD Learning to learn quickly for few shot learning","year":"2017","author":"li","key":"ref13"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1145\/3210240.3210337"},{"journal-title":"Learning efficient convolutional networks through network slimming","year":"2017","author":"liu","key":"ref15"},{"key":"ref16","first-page":"1","volume":"31","author":"lu","year":"2016","journal-title":"The development of Deep Convolution Neural Network and Its Applications on Computer Vision"},{"journal-title":"Thinet A filter level pruning method for deep neural network compression","year":"2017","author":"luo","key":"ref17"},{"key":"ref18","article-title":"Three approaches for personalization with applications to federated learning","author":"mansour","year":"2020","journal-title":"CoRR vol abs\/2002 10619"},{"journal-title":"Exploring the regularity of sparse structure in convolutional neural networks","year":"2017","author":"mao","key":"ref19"},{"key":"ref28","article-title":"Efficient meta learning via minibatch proximal update","volume":"32","author":"zhou","year":"2019","journal-title":"Advances in Neural Information Pro-cessing Systems"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/DAC18072.2020.9218710"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.114185"},{"key":"ref3","article-title":"Fed-erated learning with personalization layers","author":"arivazhagan","year":"1912","journal-title":"CoRR"},{"key":"ref6","article-title":"Personalized federated learning with moreau envelopes","author":"dinh","year":"0","journal-title":"CoRR vol abs\/2006 08848 2020"},{"key":"ref5","article-title":"Adaptive personalized federated learning","author":"deng","year":"0","journal-title":"CoRR vol abs\/2003 13461 2020"},{"key":"ref8","article-title":"Personalized federated learning: A meta-learning approach","author":"fallah","year":"2020","journal-title":"CoRR vol abs\/2002 07948"},{"journal-title":"On the convergence theory of gradient-based model-agnostic meta-learning algorithms","year":"2020","author":"fallah","key":"ref7"},{"journal-title":"Structured pruning of deep convolutional neural networks","year":"2015","author":"anwar","key":"ref2"},{"key":"ref9","article-title":"The lottery ticket hypothesis: Finding sparse, trainable neural networks","author":"frankle","year":"2019","journal-title":"7th International Conference on Learning Representations ICLR 2019"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D17-1045"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/MICRO50266.2020.00030"},{"journal-title":"Coordinating filters for faster deep neural networks","year":"2017","author":"wen","key":"ref22"},{"journal-title":"Learning intrinsic sparse structures within long short-term memory","year":"2018","author":"wen","key":"ref21"},{"journal-title":"Accelerating cnn training by pruning activation gradients","year":"2020","author":"ye","key":"ref24"},{"key":"ref23","first-page":"1508","article-title":"Terngrad: Ternary gradients to reduce communication in distributed deep learning","author":"wen","year":"2017","journal-title":"Proceedings of the 31st International Conference on Neural Information Processing Systems ser NIPS'17 Red Hook"},{"key":"ref26","article-title":"Personalized federated learning with first order model optimization","author":"zhang","year":"2020","journal-title":"CoRR vol abs\/2012 08565"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1145\/3307650.3322263"}],"event":{"name":"2022 International Joint Conference on Neural Networks (IJCNN)","start":{"date-parts":[[2022,7,18]]},"location":"Padua, Italy","end":{"date-parts":[[2022,7,23]]}},"container-title":["2022 International Joint Conference on Neural Networks (IJCNN)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9891857\/9889787\/09892137.pdf?arnumber=9892137","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,3]],"date-time":"2022-11-03T22:57:12Z","timestamp":1667516232000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9892137\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,18]]},"references-count":28,"URL":"https:\/\/doi.org\/10.1109\/ijcnn55064.2022.9892137","relation":{},"subject":[],"published":{"date-parts":[[2022,7,18]]}}}