{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,27]],"date-time":"2025-07-27T07:49:14Z","timestamp":1753602554626,"version":"3.28.0"},"reference-count":18,"publisher":"IEEE","license":[{"start":{"date-parts":[[2022,5,23]],"date-time":"2022-05-23T00:00:00Z","timestamp":1653264000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,5,23]],"date-time":"2022-05-23T00:00:00Z","timestamp":1653264000000},"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":[[2022,5,23]]},"DOI":"10.1109\/icassp43922.2022.9747880","type":"proceedings-article","created":{"date-parts":[[2022,4,27]],"date-time":"2022-04-27T19:50:34Z","timestamp":1651089034000},"page":"4358-4362","source":"Crossref","is-referenced-by-count":1,"title":["Tempo: Improving Training Performance in Cross-Silo Federated Learning"],"prefix":"10.1109","author":[{"given":"Chen","family":"Ying","sequence":"first","affiliation":[{"name":"University of Toronto"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Baochun","family":"Li","sequence":"additional","affiliation":[{"name":"University of Toronto"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bo","family":"Li","sequence":"additional","affiliation":[{"name":"Hong Kong University of Science and Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref10","article-title":"Federated Learning with Matched Averaging","author":"wang","year":"2020","journal-title":"Proc 8th International Conference on Learning Representations (ICLR)"},{"key":"ref11","article-title":"Federated Optimization in Heterogeneous Networks","author":"li","year":"2020","journal-title":"Proc 3rd Conference on Machine Learning and Systems (MLSys)"},{"key":"ref12","first-page":"5132","article-title":"SCAFFOLD: Stochastic Controlled Averaging for Federated Learning","volume":"119","author":"karimireddy","year":"2020","journal-title":"Proc of the International Conference on Machine Learning (ICML)"},{"key":"ref13","article-title":"Communication-Efficient Distributed Learning via Lazily Aggregated Quantized Gradients","author":"sun","year":"2019","journal-title":"Neural Information Processing Systems (NeurIPS)"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM41043.2020.9155494"},{"key":"ref15","first-page":"493","article-title":"BatchCrypt: Efficient Homomorphic Encryption for Cross-Silo Federated Learning","author":"zhang","year":"2020","journal-title":"Proc USENIX Annual Technical Conference (USENIX ATC 20)"},{"key":"ref16","doi-asserted-by":"crossref","first-page":"7865","DOI":"10.1609\/aaai.v35i9.16960","article-title":"Personalized Cross-Silo Federated Learning on Non-IID Data","volume":"35","author":"huang","year":"2021","journal-title":"Proc AAAI Conference on Artificial Intelligence"},{"key":"ref17","first-page":"1273","article-title":"Communication-Efficient Learning of Deep Networks from Decentralized Data","volume":"54","author":"mcmahan","year":"2017","journal-title":"Proc 20th International Conference on Artificial Intelligence and Statistics (AISTATS)"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TCCN.2021.3084406"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-019-0583-3"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3357878"},{"key":"ref6","article-title":"Federated Learning: Strategies for Improving Communication Efficiency","author":"kone?n\u00fd","year":"2016","journal-title":"Proc Neural Information Processing Systems (NIPS) Workshop on Private Multi-Party Machine Learning (PMPML)"},{"key":"ref5","article-title":"HHHFL: Hierarchical Heterogeneous Horizontal Federated Learning for Electroencephalography","author":"gao","year":"2019","journal-title":"Proc Int&#x2019;l Workshop on Federated Machine Learning for User Privacy and Data Confidentiality in Conjunction with IJCAI 2019 (FLIJCAI&#x2019;2019)"},{"key":"ref8","article-title":"Robust and Communication-Efficient Federated Learning from Non-iid Data","author":"sattler","year":"2019","journal-title":"IEEE Trans Neural Networks and Learning Systems"},{"article-title":"Federated Learning with Non-IID Data","year":"2018","author":"zhao","key":"ref7"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098118"},{"article-title":"Advances and Open Problems in Federated Learning","year":"2019","author":"kairouz","key":"ref1"},{"key":"ref9","article-title":"Federated Optimization: Distributed Optimization Beyond the Datacenter","author":"kone?n\u00fd","year":"2015","journal-title":"Proc 8th NIPS Workshop on Optimization for Machine Learning (OPT)"}],"event":{"name":"ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","start":{"date-parts":[[2022,5,23]]},"location":"Singapore, Singapore","end":{"date-parts":[[2022,5,27]]}},"container-title":["ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9745891\/9746004\/09747880.pdf?arnumber=9747880","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,23]],"date-time":"2024-09-23T03:19:48Z","timestamp":1727061588000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9747880\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,23]]},"references-count":18,"URL":"https:\/\/doi.org\/10.1109\/icassp43922.2022.9747880","relation":{},"subject":[],"published":{"date-parts":[[2022,5,23]]}}}