{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T18:50:24Z","timestamp":1768071024636,"version":"3.49.0"},"publisher-location":"New York, NY, USA","reference-count":13,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,1,8]],"date-time":"2022-01-08T00:00:00Z","timestamp":1641600000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,1,8]]},"DOI":"10.1145\/3493700.3493750","type":"proceedings-article","created":{"date-parts":[[2022,1,7]],"date-time":"2022-01-07T23:54:21Z","timestamp":1641599661000},"page":"298-299","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["Fair Federated Learning for Heterogeneous Data"],"prefix":"10.1145","author":[{"given":"Samhita","family":"Kanaparthy","sequence":"first","affiliation":[{"name":"Machine Learning Lab, International Institute of Information Technology Hyderabad, India"}]},{"given":"Manisha","family":"Padala","sequence":"additional","affiliation":[{"name":"Machine Learning Lab, International Institute of Information Technology Hyderabad, India"}]},{"given":"Sankarshan","family":"Damle","sequence":"additional","affiliation":[{"name":"Machine Learning Lab, International Institute of Information Technology Hyderabad, India"}]},{"given":"Sujit","family":"Gujar","sequence":"additional","affiliation":[{"name":"Machine Learning Lab, International Institute of Information Technology Hyderabad, India"}]}],"member":"320","published-online":{"date-parts":[[2022,1,8]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"crossref","unstructured":"Alexandra Chouldechova. 2017. Fair prediction with disparate impact: A study of bias in recidivism prediction instruments. Big data 5 2(2017) 153\u2013163.","DOI":"10.1089\/big.2016.0047"},{"key":"e_1_3_2_1_2_1","volume-title":"FairFed: Cross-Device Fair Federated Learning. In 2020 IEEE Applied Imagery Pattern Recognition Workshop (AIPR). 1\u20137.","author":"Rehman Muhammad Habib\u00a0ur","year":"2020","unstructured":"Muhammad Habib\u00a0ur Rehman, Ahmed Mukhtar\u00a0Dirir, Khaled Salah, and Davor Svetinovic. 2020. FairFed: Cross-Device Fair Federated Learning. In 2020 IEEE Applied Imagery Pattern Recognition Workshop (AIPR). 1\u20137."},{"key":"e_1_3_2_1_3_1","unstructured":"Andrew Hard Kanishka Rao Rajiv Mathews Fran\u00e7oise Beaufays Sean Augenstein Hubert Eichner Chlo\u00e9 Kiddon and Daniel Ramage. 2018. Federated Learning for Mobile Keyboard Prediction. ArXiv abs\/1811.03604(2018)."},{"key":"e_1_3_2_1_4_1","unstructured":"Samhita Kanaparthy Padala Manisha Sankarshan Damle and Sujit Gujar. 2021. Fair Federated Learning for Heterogeneous Face Data. CoRR abs\/2109.02351(2021). arXiv:2109.02351https:\/\/arxiv.org\/abs\/2109.02351"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2020.106854"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.425"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58610-2_22"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"crossref","unstructured":"Padala Manisha Sankarshan Damle and Sujit Gujar. 2021. Federated Learning Meets Fairness and Differential Privacy. ArXiv abs\/2108.09932(2021).","DOI":"10.1007\/978-3-030-92310-5_80"},{"key":"e_1_3_2_1_9_1","unstructured":"Brendan McMahan Eider Moore Daniel Ramage Seth Hampson and Blaise\u00a0Aguera y Arcas. 2017. Communication-efficient learning of deep networks from decentralized data. In Artificial intelligence and statistics. PMLR 1273\u20131282."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2020\/315"},{"key":"e_1_3_2_1_11_1","volume-title":"Fairness definitions explained. In 2018 ieee\/acm international workshop on software fairness (fairware)","author":"Verma Sahil","unstructured":"Sahil Verma and Julia Rubin. 2018. Fairness definitions explained. In 2018 ieee\/acm international workshop on software fairness (fairware). IEEE, 1\u20137."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2021.3058573"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.463"}],"event":{"name":"CODS-COMAD 2022: 5th Joint International Conference on Data Science & Management of Data (9th ACM IKDD CODS and 27th COMAD)","location":"Bangalore India","acronym":"CODS-COMAD 2022","sponsor":["SIGGRAPH ACM Special Interest Group on Computer Graphics and Interactive Techniques"]},"container-title":["Proceedings of the 5th Joint International Conference on Data Science &amp; Management of Data (9th ACM IKDD CODS and 27th COMAD)"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3493700.3493750","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3493700.3493750","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:11:51Z","timestamp":1750191111000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3493700.3493750"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,8]]},"references-count":13,"alternative-id":["10.1145\/3493700.3493750","10.1145\/3493700"],"URL":"https:\/\/doi.org\/10.1145\/3493700.3493750","relation":{},"subject":[],"published":{"date-parts":[[2022,1,8]]},"assertion":[{"value":"2022-01-08","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}