{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T01:59:09Z","timestamp":1772157549055,"version":"3.50.1"},"reference-count":19,"publisher":"Totem Publisher, Inc.","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int J Performability Eng"],"published-print":{"date-parts":[[2026]]},"DOI":"10.23940\/ijpe.26.03.p4.149157","type":"journal-article","created":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T01:18:22Z","timestamp":1772155102000},"page":"149","source":"Crossref","is-referenced-by-count":0,"title":["Federated Learning for Heterogeneous Multimodal Emotion Recognition on Edge Devices"],"prefix":"10.23940","volume":"22","author":[{"given":"Sharma","family":"Bhawana","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Saxena","family":"Komal","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10023","reference":[{"key":"key-10.23940\/ijpe.26.03.p4.149157-1","doi-asserted-by":"crossref","unstructured":"Torous J., Myrick K.J., Rauseo-Ricupero N., and Firth J., 2020. Digital mental health and COVID-19: using technology today to accelerate the curve on access and quality tomorrow. JMIR Mental Health, 7(3), e18848.","DOI":"10.2196\/18848"},{"key":"key-10.23940\/ijpe.26.03.p4.149157-2","doi-asserted-by":"crossref","unstructured":"Insel T.R., 2017. Digital phenotyping: technology for a new science of behavior. Jama, 318(13), pp. 1215-1216.","DOI":"10.1001\/jama.2017.11295"},{"key":"key-10.23940\/ijpe.26.03.p4.149157-3","doi-asserted-by":"crossref","unstructured":"Huckvale K., Torous J., and Larsen M.E., 2019. Assessment of the data sharing and privacy practices of smartphone apps for depression and smoking cessation. JAMA Network Open, 2(4), pp. e192542-e192542.","DOI":"10.1001\/jamanetworkopen.2019.2542"},{"key":"key-10.23940\/ijpe.26.03.p4.149157-4","doi-asserted-by":"crossref","unstructured":"Kostkova P., Brewer H., De Lusignan S., Fottrell E., Goldacre B., Hart G., Koczan P., Knight P., Marsolier C., McKendry R.A., and Ross E., 2016. Who owns the data? open data for healthcare. Frontiers in Public Health, 4, 7.","DOI":"10.3389\/fpubh.2016.00007"},{"key":"key-10.23940\/ijpe.26.03.p4.149157-5","unstructured":"McMahan B., Moore E., Ramage D., Hampson S., and y Arcas B.A., 2017. Communication-efficient learning of deep networks from decentralized data. In\n                      Artificial Intelligence and Statistics\n                      , pp. 1273-1282."},{"key":"key-10.23940\/ijpe.26.03.p4.149157-6","doi-asserted-by":"crossref","unstructured":"Yang Q., Liu Y., Chen T., and Tong Y., 2019. Federated machine learning: concept and applications. ACM Transactions on Intelligent Systems and Technology (TIST), 10(2), pp. 1-19.","DOI":"10.1145\/3298981"},{"key":"key-10.23940\/ijpe.26.03.p4.149157-7","unstructured":"Radford A., Kim J.W., Hallacy C., Ramesh A., Goh G., Agarwal S., Sastry G., Askell A., Mishkin P., Clark J., and Krueger G., 2021. Learning transferable visual models from natural language supervision. In\n                      International Conference on Machine Learning\n                      , pp. 8748-8763."},{"key":"key-10.23940\/ijpe.26.03.p4.149157-8","doi-asserted-by":"crossref","unstructured":"Li T., Sahu A.K., Talwalkar A., and Smith V., 2020. Federated learning: challenges, methods, and future directions. IEEE Signal Processing Magazine, 37(3), pp. 50-60.","DOI":"10.1109\/MSP.2020.2975749"},{"key":"key-10.23940\/ijpe.26.03.p4.149157-9","doi-asserted-by":"crossref","unstructured":"Kairouz P., McMahan H.B., Avent B., Bellet A., Bennis M., Bhagoji A.N., Bonawitz K., Charles Z., Cormode G., Cummings R., and D\u2019Oliveira R.G., 2021. Advances and open problems in federated learning. Foundations and Trends\u00ae in Machine Learning, 14(1-2), pp. 1-210.","DOI":"10.1561\/2200000083"},{"key":"key-10.23940\/ijpe.26.03.p4.149157-10","unstructured":"Zhao Y., Li M., Lai L., Suda N., Civin D., and Chandra V., 2018. Federated learning with non-iid data.\n                      Arxiv Preprint Arxiv:1806\n                      .\n                      00582\n                      ."},{"key":"key-10.23940\/ijpe.26.03.p4.149157-11","doi-asserted-by":"crossref","unstructured":"Sun Z., Yu H., Song X., Liu R., Yang Y., and Zhou D., 2020. Mobilebert: a compact task-agnostic Bert for resource-limited devices.\n                      Arxiv Preprint Arxiv:2004\n                      .\n                      02984\n                      .","DOI":"10.18653\/v1\/2020.acl-main.195"},{"key":"key-10.23940\/ijpe.26.03.p4.149157-12","unstructured":"Mehta S., and Rastegari M., 2021. Mobilevit: light-weight, general-purpose, and mobile-friendly vision transformer.\n                      Arxiv Preprint Arxiv:2110\n                      .\n                      02178\n                      ."},{"key":"key-10.23940\/ijpe.26.03.p4.149157-13","doi-asserted-by":"crossref","unstructured":"Demszky D., Movshovitz-Attias D., Ko J., Cowen A., Nemade G., and Ravi S., 2020. GoEmotions: A dataset of fine-grained emotions.\n                      Arxiv Preprint Arxiv:2005\n                      .\n                      00547\n                      .","DOI":"10.18653\/v1\/2020.acl-main.372"},{"key":"key-10.23940\/ijpe.26.03.p4.149157-14","unstructured":"Mishra S., Suryavardan S., Patwa P., Chakraborty M., Rani A., Reganti A., Chadha A., Das A., Sheth A., Chinnakotla M., and Ekbal A., 2023. Memotion 3: dataset on sentiment and emotion analysis of codemixed hindi-english memes.\n                      Arxiv Preprint Arxiv:2303\n                      .\n                      09892\n                      ."},{"key":"key-10.23940\/ijpe.26.03.p4.149157-15","doi-asserted-by":"crossref","unstructured":"Akiba T., Sano S., Yanase T., Ohta T., and Koyama M., 2019. Optuna: A next-generation hyperparameter optimization framework. In\n                      Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining\n                      , pp. 2623-2631.","DOI":"10.1145\/3292500.3330701"},{"key":"key-10.23940\/ijpe.26.03.p4.149157-16","unstructured":"Kone\u010dn\u00fd J., McMahan H.B., Yu F.X., Richt\u00e1rik P., Suresh A.T., and Bacon D., 2016. Federated learning: strategies for improving communication efficiency.\n                      Arxiv Preprint Arxiv:1610\n                      .\n                      05492\n                      ."},{"key":"key-10.23940\/ijpe.26.03.p4.149157-17","unstructured":"Bonawitz K., Eichner H., Grieskamp W., Huba D., Ingerman A., Ivanov V., Kiddon C., Kone\u010dn\u00fd J., Mazzocchi S., McMahan B., and Van Overveldt T., 2019. Towards federated learning at scale: system design. Proceedings of Machine Learning and Systems, 1, pp. 374-388."},{"key":"key-10.23940\/ijpe.26.03.p4.149157-18","unstructured":"Li T., Sahu A.K., Zaheer M., Sanjabi M., Talwalkar A., and Smith V., 2020. Federated optimization in heterogeneous networks. Proceedings of Machine Learning and Systems, 2, pp. 429-450."},{"key":"key-10.23940\/ijpe.26.03.p4.149157-19","doi-asserted-by":"crossref","unstructured":"Baltruaitis T., Ahuja C., and Morency L.P., 2019. Multimodal machine learning: A survey and taxonomy. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(2), pp. 423-443.","DOI":"10.1109\/TPAMI.2018.2798607"}],"container-title":["International Journal of Performability Engineering"],"original-title":[],"language":"en","deposited":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T01:18:34Z","timestamp":1772155114000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijpe-online.com\/EN\/10.23940\/ijpe.26.03.p4.149157"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":19,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2026]]}},"URL":"https:\/\/doi.org\/10.23940\/ijpe.26.03.p4.149157","relation":{},"ISSN":["0973-1318"],"issn-type":[{"value":"0973-1318","type":"print"}],"subject":[],"published":{"date-parts":[[2026]]}}}