{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T08:57:43Z","timestamp":1785488263043,"version":"3.56.0"},"publisher-location":"New York, NY, USA","reference-count":39,"publisher":"ACM","license":[{"start":{"date-parts":[[2025,12,17]],"date-time":"2025-12-17T00:00:00Z","timestamp":1765929600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,12,17]]},"DOI":"10.1145\/3774521.3774529","type":"proceedings-article","created":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T07:34:24Z","timestamp":1785483264000},"page":"1-10","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["VAD-FedHSM: Video Anomaly Detection in Federated Learning Framework with Local Hard Sample Mining"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-6750-9256","authenticated-orcid":false,"given":"Dillip","family":"Sathiyamoorthy","sequence":"first","affiliation":[{"name":"Electronics and Electrical Engineering, Indian Institute of Technology Guwahati, Guwahati, Assam, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2885-0026","authenticated-orcid":false,"given":"Prithwijit","family":"Guha","sequence":"additional","affiliation":[{"name":"Electronics and Electrical Engineering, Indian Institute of Technology Guwahati, Guwahati, Assam, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,7,31]]},"reference":[{"key":"e_1_3_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/WACV57701.2024.00665"},{"key":"e_1_3_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.01180"},{"key":"e_1_3_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i1.25112"},{"key":"e_1_3_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/DSC53242.2023.00012"},{"key":"e_1_3_3_3_6_2","doi-asserted-by":"crossref","unstructured":"Christoph Feichtenhofer. 2020. X3D: Expanding Architectures for Efficient Video Recognition. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2004.04730 (2020). https:\/\/arxiv.org\/abs\/2004.04730","DOI":"10.1109\/CVPR42600.2020.00028"},{"key":"e_1_3_3_3_7_2","unstructured":"Andrew Gao and Jun Liu. 2025. STEAD: Spatio-Temporal Efficient Anomaly Detection for Time and Compute Sensitive Applications. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2503.07942 (2025). https:\/\/arxiv.org\/abs\/2503.07942"},{"key":"e_1_3_3_3_8_2","doi-asserted-by":"publisher","unstructured":"Giacomo Giorgi Wisam Abbasi and Andrea Saracino. 2022. Privacy-Preserving Analysis for Remote Video Anomaly Detection in Real Life Environments. Journal of Wireless Mobile Networks Ubiquitous Computing and Dependable Applications (2022). 10.22667\/JOWUA.2022.03.31.112","DOI":"10.22667\/JOWUA.2022.03.31.112"},{"key":"e_1_3_3_3_9_2","doi-asserted-by":"publisher","unstructured":"James\u00a0A. Hanley and Barbara\u00a0J. McNeil. 1982. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143 1 (1982) 29\u201336. 10.1148\/radiology.143.1.7063747","DOI":"10.1148\/radiology.143.1.7063747"},{"key":"e_1_3_3_3_10_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01261-8_19"},{"key":"e_1_3_3_3_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP47345.2023.10224735"},{"key":"e_1_3_3_3_12_2","volume-title":"Proceedings of MLSys","author":"Li Tian","year":"2020","unstructured":"Tian Li, Anit\u00a0Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith. 2020. Federated Optimization in Heterogeneous Networks. In Proceedings of MLSys. https:\/\/arxiv.org\/abs\/1812.06127"},{"key":"e_1_3_3_3_13_2","unstructured":"Jing Liu Yang Liu and Xiaoguang Zhu. 2024. Privacy-Preserving Video Anomaly Detection: A Survey. arxiv:https:\/\/arXiv.org\/abs\/2411.14565\u00a0[cs.CV] https:\/\/arxiv.org\/abs\/2411.14565"},{"key":"e_1_3_3_3_14_2","doi-asserted-by":"publisher","unstructured":"Shu Liu Jian Cheng Zhiwei Xia Zhi Xi Qi Hou and Zhi Dong. 2024. HCM: Online Action Detection with Hard Video Clip Mining. IEEE Transactions on Multimedia 26 1 (2024) 123\u2013134. 10.1109\/TMM.2023.3313258","DOI":"10.1109\/TMM.2023.3313258"},{"key":"e_1_3_3_3_15_2","volume-title":"International Conference on Learning Representations (ICLR)","author":"Loshchilov Ilya","year":"2017","unstructured":"Ilya Loshchilov and Frank Hutter. 2017. SGDR: Stochastic Gradient Descent with Warm Restarts. In International Conference on Learning Representations (ICLR). https:\/\/openreview.net\/forum?id=Skq89Scxx"},{"key":"e_1_3_3_3_16_2","volume-title":"International Conference on Learning Representations (ICLR)","author":"Loshchilov Ilya","year":"2019","unstructured":"Ilya Loshchilov and Frank Hutter. 2019. Decoupled Weight Decay Regularization. In International Conference on Learning Representations (ICLR). https:\/\/openreview.net\/forum?id=Bkg6RiCqY7"},{"key":"e_1_3_3_3_17_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00775"},{"key":"e_1_3_3_3_18_2","unstructured":"Leland McInnes John Healy and James Melville. 2018. UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/1802.03426 (2018)."},{"key":"e_1_3_3_3_19_2","first-page":"1273","volume-title":"Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS)","author":"McMahan H.\u00a0Brendan","year":"2017","unstructured":"H.\u00a0Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Ag\u00fcera\u00a0y Arcas. 2017. Communication-Efficient Learning of Deep Networks from Decentralized Data. In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS). PMLR, 1273\u20131282. https:\/\/proceedings.mlr.press\/v54\/mcmahan17a.html"},{"key":"e_1_3_3_3_20_2","doi-asserted-by":"publisher","unstructured":"Rashmiranjan Nayak Umesh\u00a0Chandra Pati and Santos\u00a0Kumar Das. 2021. A comprehensive review on deep learning-based methods for video anomaly detection. Image and Vision Computing 106 (2021) 104078. 10.1016\/j.imavis.2020.104078","DOI":"10.1016\/j.imavis.2020.104078"},{"key":"e_1_3_3_3_21_2","doi-asserted-by":"publisher","unstructured":"Guansong Pang Chunhua Shen Longbing Cao and Anton Van\u00a0Den Hengel. 2021. Deep Learning for Anomaly Detection: A Review. ACM Comput. Surv. 54 2 (2021). 10.1145\/3439950","DOI":"10.1145\/3439950"},{"key":"e_1_3_3_3_22_2","doi-asserted-by":"publisher","DOI":"10.1145\/3465481.3470099"},{"key":"e_1_3_3_3_23_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-73668-1_14"},{"key":"e_1_3_3_3_24_2","doi-asserted-by":"publisher","unstructured":"Bharathkumar Ramachandra Michael Jones and Ranga Vatsavai. 2020. A Survey of Single-Scene Video Anomaly Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence PP (11 2020) 1\u20131. 10.1109\/TPAMI.2020.3040591","DOI":"10.1109\/TPAMI.2020.3040591"},{"key":"e_1_3_3_3_25_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDMW53433.2021.00125"},{"key":"e_1_3_3_3_26_2","doi-asserted-by":"publisher","unstructured":"Herbert Robbins and Sutton Monro. 1951. A Stochastic Approximation Method. The Annals of Mathematical Statistics 22 3 (1951) 400\u2013407. 10.1214\/aoms\/1177729586","DOI":"10.1214\/aoms\/1177729586"},{"key":"e_1_3_3_3_27_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"e_1_3_3_3_28_2","unstructured":"Neta Shoham Tomer Avidor Aviv Keren Nadav Israel Daniel Benditkis Liron Mor-Yosef and Itai Zeitak. 2019. Overcoming Forgetting in Federated Learning on Non-IID Data. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/1910.07796 (2019). https:\/\/arxiv.org\/abs\/1910.07796"},{"key":"e_1_3_3_3_29_2","doi-asserted-by":"publisher","unstructured":"Yue Su Ji Li Shuang An Mingxing Xing and Zhiwei Feng. 2025. Federated Weakly-Supervised Video Anomaly Detection with Mixture of Local-to-Global Experts. Information Fusion 75 (2025) 1\u201312. 10.1016\/j.inffus.2025.03.003","DOI":"10.1016\/j.inffus.2025.03.003"},{"key":"e_1_3_3_3_30_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00678"},{"key":"e_1_3_3_3_31_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00489"},{"key":"e_1_3_3_3_32_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v39i20.35398"},{"key":"e_1_3_3_3_33_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i6.28423"},{"key":"e_1_3_3_3_34_2","doi-asserted-by":"crossref","unstructured":"Zhiwei Yang Jing Liu and Peng Wu. 2024. Text Prompt with Normality Guidance for Weakly Supervised Video Anomaly Detection. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2404.08531 (2024). https:\/\/arxiv.org\/abs\/2404.08531","DOI":"10.1109\/CVPR52733.2024.01788"},{"key":"e_1_3_3_3_35_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01433"},{"key":"e_1_3_3_3_36_2","unstructured":"Jing Zhang Chuanwen Li Jianzgong Qi and Jiayuan He. 2023. A Survey on Class Imbalance in Federated Learning. arxiv:https:\/\/arXiv.org\/abs\/2303.11673\u00a0[cs.LG] https:\/\/arxiv.org\/abs\/2303.11673"},{"key":"e_1_3_3_3_37_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP.2019.8803476"},{"key":"e_1_3_3_3_38_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00133"},{"key":"e_1_3_3_3_39_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i3.25489"},{"key":"e_1_3_3_3_40_2","unstructured":"Hangyu Zhu Jinjin Xu Shiqing Liu and Yaochu Jin. 2021. Federated Learning on Non-IID Data: A Survey. arxiv:https:\/\/arXiv.org\/abs\/2106.06843\u00a0[cs.LG] https:\/\/arxiv.org\/abs\/2106.06843"}],"event":{"name":"ICVGIP 2025: Indian Conference on Computer Vision, Graphics, and Image Processing","location":"Mandi Himachal Pradesh India","acronym":"ICVGIP 2025"},"container-title":["Proceedings of the Sixteen Indian Conference on Computer Vision, Graphics and Image Processing"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3774521.3774529","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T08:02:06Z","timestamp":1785484926000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3774521.3774529"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,17]]},"references-count":39,"alternative-id":["10.1145\/3774521.3774529","10.1145\/3774521"],"URL":"https:\/\/doi.org\/10.1145\/3774521.3774529","relation":{},"subject":[],"published":{"date-parts":[[2025,12,17]]},"assertion":[{"value":"2026-07-31","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}