{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T15:58:57Z","timestamp":1785340737822,"version":"3.55.0"},"publisher-location":"New York, NY, USA","reference-count":23,"publisher":"ACM","license":[{"start":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T00:00:00Z","timestamp":1782777600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"name":"Norwegian Directorate for Higher Education and Skills (HKdir)","award":["UTF-2024\/10225"],"award-info":[{"award-number":["UTF-2024\/10225"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,6,30]]},"DOI":"10.1145\/3807503.3819439","type":"proceedings-article","created":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T02:55:27Z","timestamp":1785293727000},"page":"1-6","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["FedBalance: An Adaptive Cross-Silo Federated Learning Approach for Stress Detection under non-IID Data Distributions"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-2241-1404","authenticated-orcid":false,"given":"Bjarte","family":"Nerland","sequence":"first","affiliation":[{"name":"Kristiania University of Applied Sciences, Bergen, Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-6891-9999","authenticated-orcid":false,"given":"Bithi","family":"Banik","sequence":"additional","affiliation":[{"name":"Kristiania University of Applied Sciences, Bergen, Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6974-8856","authenticated-orcid":false,"given":"Yuan","family":"Lin","sequence":"additional","affiliation":[{"name":"Kristiania University of Applied Sciences, Bergen, Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6310-2192","authenticated-orcid":false,"given":"Debasish","family":"Ghose","sequence":"additional","affiliation":[{"name":"Kristiania University of Applied Sciences, Bergen, Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,7,28]]},"reference":[{"key":"e_1_3_3_2_2_2","doi-asserted-by":"crossref","unstructured":"Eman Abdelfattah Shreehar Joshi and Shreekar Tiwari. 2025. Machine and deep learning models for stress detection using multimodal physiological data. IEEE Access (2025).","DOI":"10.1109\/ACCESS.2024.3525459"},{"key":"e_1_3_3_2_3_2","doi-asserted-by":"crossref","unstructured":"Ahmad Almadhor Gabriel\u00a0Avelino Sampedro Mideth Abisado Sidra Abbas Ye-Jin Kim Muhammad\u00a0Attique Khan Jamel Baili and Jae-Hyuk Cha. 2023. Wrist-based electrodermal activity monitoring for stress detection using federated learning. Sensors 23 8 (2023) 3984.","DOI":"10.3390\/s23083984"},{"key":"e_1_3_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICHI64645.2025.00041"},{"key":"e_1_3_3_2_5_2","doi-asserted-by":"publisher","unstructured":"Bithi Banik Yuan Lin Andrii Shalaginov and Debasish Ghose. 2026. FedSmartCare: Design and Implementation of a Federated Learning Enabled Vital-Sign Monitoring System. IEEE Sensors Journal 26 9 (2026) 13948\u201313967. 10.1109\/JSEN.2026.3676888","DOI":"10.1109\/JSEN.2026.3676888"},{"key":"e_1_3_3_2_6_2","unstructured":"Daniel\u00a0J Beutel Taner Topal Akhil Mathur Xinchi Qiu Javier Fernandez-Marques Yan Gao Lorenzo Sani Kwing\u00a0Hei Li Titouan Parcollet Pedro Porto\u00a0Buarque De\u00a0Gusm\u00e3o et\u00a0al. 2020. Flower: A friendly federated learning research framework. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2007.14390 (2020)."},{"key":"e_1_3_3_2_7_2","doi-asserted-by":"crossref","unstructured":"Shruti Gedam and Sanchita Paul. 2021. A review on mental stress detection using wearable sensors and machine learning techniques. IEEE Access 9 (2021) 84045\u201384066.","DOI":"10.1109\/ACCESS.2021.3085502"},{"key":"e_1_3_3_2_8_2","doi-asserted-by":"crossref","unstructured":"Peter Kairouz and H\u00a0Brendan McMahan. 2021. Advances and open problems in federated learning. Foundations and trends in machine learning 14 1-2 (2021) 1\u2013210.","DOI":"10.1561\/2200000083"},{"key":"e_1_3_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1145\/2663204.2663257"},{"key":"e_1_3_3_2_10_2","doi-asserted-by":"crossref","unstructured":"Amit Kumar JK Barath P Shanmukh and Deepak Joshi. 2024. StreXNet: A Novel End-to-End Deep Learning Based Improved Multi-Level Mental Stress Classification from EEG Sensors. IEEE Sensors Journal (2024).","DOI":"10.1109\/JSEN.2024.3506984"},{"key":"e_1_3_3_2_11_2","unstructured":"Tian Li Anit\u00a0Kumar Sahu Manzil Zaheer Maziar Sanjabi Ameet Talwalkar and Virginia Smith. 2020. Federated optimization in heterogeneous networks. Proceedings of Machine learning and systems 2 (2020) 429\u2013450."},{"key":"e_1_3_3_2_12_2","unstructured":"Tian Li Maziar Sanjabi Ahmad Beirami and Virginia Smith. 2019. Fair resource allocation in federated learning. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/1905.10497 (2019)."},{"key":"e_1_3_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP48485.2024.10446004"},{"key":"e_1_3_3_2_14_2","unstructured":"Brendan McMahan and Daniel Ramage. 2017. Federated learning: Collaborative machine learning without centralized training data. Google Research Blog 3 (2017)."},{"key":"e_1_3_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDICI66477.2025.11134972"},{"key":"e_1_3_3_2_16_2","doi-asserted-by":"crossref","unstructured":"Jon\u00a0Andreas Mortensen Martin\u00a0Efremov Mollov Ayan Chatterjee Debasish Ghose and Frank\u00a0Y Li. 2023. Multi-class stress detection through heart rate variability: A deep neural network based study. IEEE Access 11 (2023) 57470\u201357480.","DOI":"10.1109\/ACCESS.2023.3274478"},{"key":"e_1_3_3_2_17_2","doi-asserted-by":"crossref","unstructured":"Stephan\u00a0J Motowidlo John\u00a0S Packard and Michael\u00a0R Manning. 1986. Occupational stress: its causes and consequences for job performance. Journal of applied psychology 71 4 (1986) 618.","DOI":"10.1037\/0021-9010.71.4.618"},{"key":"e_1_3_3_2_18_2","doi-asserted-by":"crossref","unstructured":"Suraj Rajendran Zhenxing Xu Weishen Pan Arnab Ghosh and Fei Wang. 2023. Data heterogeneity in federated learning with Electronic Health Records: Case studies of risk prediction for acute kidney injury and sepsis diseases in critical care. PLOS Digital Health 2 3 (2023) e0000117.","DOI":"10.1371\/journal.pdig.0000117"},{"key":"e_1_3_3_2_19_2","doi-asserted-by":"crossref","unstructured":"Nafiul Rashid Trier Mortlock and Mohammad\u00a0Abdullah Al\u00a0Faruque. 2023. Stress detection using context-aware sensor fusion from wearable devices. IEEE Internet of Things Journal 10 16 (2023) 14114\u201314127.","DOI":"10.1109\/JIOT.2023.3265768"},{"key":"e_1_3_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1145\/3242969.3242985"},{"key":"e_1_3_3_2_21_2","unstructured":"Jungwon Seo Ferhat\u00a0Ozgur Catak and Chunming Rong. 2025. Understanding federated learning from iid to non-iid dataset: An experimental study. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2502.00182 (2025)."},{"key":"e_1_3_3_2_22_2","doi-asserted-by":"crossref","unstructured":"Wonju Seo Namho Kim Cheolsoo Park and Sung-Min Park. 2022. Deep learning approach for detecting work-related stress using multimodal signals. IEEE Sensors Journal 22 12 (2022) 11892\u201311902.","DOI":"10.1109\/JSEN.2022.3170915"},{"key":"e_1_3_3_2_23_2","unstructured":"Jianyu Wang Qinghua Liu Hao Liang Gauri Joshi and H\u00a0Vincent Poor. 2020. Tackling the objective inconsistency problem in heterogeneous federated optimization. Advances in neural information processing systems 33 (2020) 7611\u20137623."},{"key":"e_1_3_3_2_24_2","doi-asserted-by":"crossref","unstructured":"Qiang Yang Yang Liu Tianjian Chen and Yongxin Tong. 2019. Federated machine learning: Concept and applications. ACM Transactions on Intelligent Systems and Technology (TIST) 10 2 (2019) 1\u201319.","DOI":"10.1145\/3298981"}],"event":{"name":"BCB '26: 17th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics","location":"Rende (CS) Italy","acronym":"BCB '26","sponsor":["SIGBio ACM Special Interest Group on Bioinformatics"]},"container-title":["Proceedings of the 17th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3807503.3819439","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T15:10:36Z","timestamp":1785337836000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3807503.3819439"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,30]]},"references-count":23,"alternative-id":["10.1145\/3807503.3819439","10.1145\/3807503"],"URL":"https:\/\/doi.org\/10.1145\/3807503.3819439","relation":{},"subject":[],"published":{"date-parts":[[2026,6,30]]},"assertion":[{"value":"2026-07-28","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}