{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T13:53:28Z","timestamp":1784555608074,"version":"3.55.0"},"reference-count":26,"publisher":"IEEE","license":[{"start":{"date-parts":[[2019,12,1]],"date-time":"2019-12-01T00:00:00Z","timestamp":1575158400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2019,12,1]],"date-time":"2019-12-01T00:00:00Z","timestamp":1575158400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2019,12,1]],"date-time":"2019-12-01T00:00:00Z","timestamp":1575158400000},"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":[[2019,12]]},"DOI":"10.1109\/bigdata47090.2019.9006327","type":"proceedings-article","created":{"date-parts":[[2020,2,25]],"date-time":"2020-02-25T06:05:34Z","timestamp":1582610734000},"page":"2577-2586","source":"Crossref","is-referenced-by-count":215,"title":["Profit Allocation for Federated Learning"],"prefix":"10.1109","author":[{"given":"Tianshu","family":"Song","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongxin","family":"Tong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuyue","family":"Wei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.12"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1145\/3243734.3243760"},{"key":"ref12","first-page":"1711","article-title":"Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption","volume":"abs","author":"hardy","year":"2017","journal-title":"CoRR"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-93387-0_13"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1145\/2976749.2978318"},{"key":"ref15","first-page":"1712","article-title":"Differentially private federated learning: A client level perspective","volume":"abs","author":"geyer","year":"2017","journal-title":"CoRR"},{"key":"ref16","article-title":"Learning differentially private recurrent language models","author":"mcmahan","year":"2018","journal-title":"6th International Conference on Learning Representations ICLR 2018"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1145\/3289600.3290968"},{"key":"ref18","article-title":"Federated optimization: Distributed machine learning for on-device intelligence","volume":"abs 1610 2527","author":"konecn\u00fd","year":"2016","journal-title":"CoRR"},{"key":"ref19","article-title":"Federated learning for emoji prediction in a mobile keyboard","volume":"abs 1906 4329","author":"ramaswamy","year":"2019","journal-title":"CoRR"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1515\/9781400881970-018"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1145\/3298981"},{"key":"ref6","first-page":"2242","author":"ghorbani","year":"2019","journal-title":"Data shapley Equitable valuation of data for machine learning"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.2307\/j.ctvjsf522"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.14778\/3342263.3342637"},{"key":"ref7","first-page":"1167","article-title":"Towards efficient data valuation based on the shapley value","author":"jia","year":"2019","journal-title":"International Conference on Artificial Intelligence and Statistics"},{"key":"ref2","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","author":"mcmahan","year":"2017","journal-title":"Proc of the International Conference on Artificial Intelligence and Statistics"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1145\/3133956.3133982"},{"key":"ref1","year":"2016","journal-title":"General Data Protection Regulation"},{"key":"ref20","first-page":"7252","article-title":"Bayesian nonparametric federated learning of neural networks","author":"yurochkin","year":"2019","journal-title":"Proceedings of the 36th International Conference on Machine Learning ICML 2019 9-15 June 2019"},{"key":"ref22","article-title":"owards federated learning at scale: System design","volume":"abs 1902 1046","author":"bonawitz","year":"2019","journal-title":"CoRR"},{"key":"ref21","first-page":"4615","article-title":"Agnostic federated learning","author":"mohri","year":"2019","journal-title":"Proceedings of the 36th International Conference on Machine Learning ICML 2019 9-15 June 2019"},{"key":"ref24","year":"0","journal-title":"Tensorflow Federated"},{"key":"ref23","article-title":"TensorFlow: Large-scale machine learning on heterogeneous systems","author":"abadi","year":"2015","journal-title":"software available from tensorflow org"},{"key":"ref26","article-title":"Mathematica, version 11.2","author":"inc","year":"2017","journal-title":"champaign IL"},{"key":"ref25","article-title":"The MNIST Database","author":"lecun","year":"0"}],"event":{"name":"2019 IEEE International Conference on Big Data (Big Data)","location":"Los Angeles, CA, USA","start":{"date-parts":[[2019,12,9]]},"end":{"date-parts":[[2019,12,12]]}},"container-title":["2019 IEEE International Conference on Big Data (Big Data)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/8986695\/9005444\/09006327.pdf?arnumber=9006327","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,17]],"date-time":"2022-07-17T21:48:12Z","timestamp":1658094492000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9006327\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12]]},"references-count":26,"URL":"https:\/\/doi.org\/10.1109\/bigdata47090.2019.9006327","relation":{},"subject":[],"published":{"date-parts":[[2019,12]]}}}