{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,14]],"date-time":"2024-08-14T05:56:54Z","timestamp":1723615014794},"reference-count":17,"publisher":"IEEE","license":[{"start":{"date-parts":[[2023,8,24]],"date-time":"2023-08-24T00:00:00Z","timestamp":1692835200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,8,24]],"date-time":"2023-08-24T00:00:00Z","timestamp":1692835200000},"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":[[2023,8,24]]},"DOI":"10.23919\/wiopt58741.2023.10349854","type":"proceedings-article","created":{"date-parts":[[2023,12,22]],"date-time":"2023-12-22T19:18:56Z","timestamp":1703272736000},"source":"Crossref","is-referenced-by-count":1,"title":["Tackling Privacy Heterogeneity in Federated Learning"],"prefix":"10.23919","author":[{"given":"Ruichen","family":"Xu","sequence":"first","affiliation":[{"name":"The Chinese University of Hong Kong,Department of Information Engineering,Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ying-Jun","family":"Angela Zhang","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong,Department of Information Engineering,Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianwei","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Science and Engineering, Shenzhen Institute of Artificial Intelligence and Robotics for Society, The Chinese University of Hong Kong, Shenzhen,Shenzhen,China,518172"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","article-title":"Deep leakage from gradients","author":"Zhu","year":"2019","journal-title":"Advances in neural information processing systems"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1145\/3494834.3500240"},{"key":"ref3","article-title":"Shuffled model of differential privacy in federated learning","volume-title":"International Conference on Artificial Intelligence and Statistics","author":"Girgis","year":"2021"},{"key":"ref4","article-title":"Understanding clipping for federated learning: Convergence and client-level differential privacy","author":"Zhang","year":"2021","journal-title":"arXiv preprint"},{"key":"ref5","article-title":"Voting-based approaches for differentially private federated learning","author":"Zhu","year":"2020","journal-title":"arXiv preprint"},{"key":"ref6","article-title":"Automatic clipping: Differentially private deep learning made easier and stronger","author":"Bu","year":"2022","journal-title":"arXiv preprint"},{"key":"ref7","article-title":"Learning differ-entially private recurrent language models","volume-title":"International Conference on Learning Representations","author":"McMahan","year":"2018"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1145\/2976749.2978318"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1561\/0400000042"},{"key":"ref10","article-title":"Communication-efficient learning of deep networks from decentralized data","author":"McMahan","year":"2017","journal-title":"Artificial intelligence and statistics"},{"key":"ref11","article-title":"Privacy amplification by sub-sampling: Tight analyses via couplings and divergences","author":"Balle","year":"2018","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/tit.2017.2685505"},{"key":"ref13","article-title":"On the convergence of fedavg on non-iid data","volume-title":"International Conference on Learning Representations","author":"Li","year":"2020"},{"key":"ref14","article-title":"Local sgd: Unified theory and new efficient methods","volume-title":"International Conference on Artificial Intelligence and Statistics","author":"Gorbunov","year":"2021"},{"key":"ref15","article-title":"Differential privacy has disparate impact on model accuracy","author":"Bagdasaryan","year":"2019","journal-title":"Advances in Neural Infor-mation Processing Systems"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref17","article-title":"Scaffold: Stochastic controlled averaging for federated learning","volume-title":"International Conference on Machine Learning","author":"Karimireddy","year":"2020"}],"event":{"name":"2023 21st International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt)","location":"Singapore, Singapore","start":{"date-parts":[[2023,8,24]]},"end":{"date-parts":[[2023,8,27]]}},"container-title":["2023 21st International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/10349702\/10349703\/10349854.pdf?arnumber=10349854","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T01:43:42Z","timestamp":1705023822000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10349854\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,24]]},"references-count":17,"URL":"https:\/\/doi.org\/10.23919\/wiopt58741.2023.10349854","relation":{},"subject":[],"published":{"date-parts":[[2023,8,24]]}}}