{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T15:45:28Z","timestamp":1782834328681,"version":"3.54.5"},"reference-count":37,"publisher":"IEEE","license":[{"start":{"date-parts":[[2022,8,21]],"date-time":"2022-08-21T00:00:00Z","timestamp":1661040000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,8,21]],"date-time":"2022-08-21T00:00:00Z","timestamp":1661040000000},"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":[[2022,8,21]]},"DOI":"10.1109\/icpr56361.2022.9956655","type":"proceedings-article","created":{"date-parts":[[2022,11,29]],"date-time":"2022-11-29T19:34:13Z","timestamp":1669750453000},"page":"996-1003","source":"Crossref","is-referenced-by-count":2,"title":["Enhancing Robustness in Federated Learning by Supervised Anomaly Detection"],"prefix":"10.1109","author":[{"given":"Pengrui","family":"Quan","sequence":"first","affiliation":[{"name":"University of California,Los Angeles"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei-Han","family":"Lee","sequence":"additional","affiliation":[{"name":"IBM Thomas J. Watson Research Center"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mudhakar","family":"Srivatsa","sequence":"additional","affiliation":[{"name":"IBM Thomas J. Watson Research Center"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mani","family":"Srivastava","sequence":"additional","affiliation":[{"name":"University of California,Los Angeles"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref33","article-title":"Dba:Distributed backdoor attacks against federated learning","author":"xie","year":"2019","journal-title":"International Conference on Learning Representations"},{"key":"ref32","article-title":"Crfl:Certifiably robust federated learning against backdoor attacks","author":"xie","year":"2021"},{"key":"ref31","first-page":"681","article-title":"Bayesian learning via stochastic gradient langevin dynamics","author":"welling","year":"2011","journal-title":"Proceedings of the 28th International Conference on Machine Learning (ICML-11)"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-65745-1_12"},{"key":"ref37","article-title":"Federated learning with non-iid data","author":"zhao","year":"2018"},{"key":"ref36","first-page":"5650","article-title":"Byzantine-robust distributed learning:Towards optimal statistical rates","author":"yin","year":"2018","journal-title":"International Conference on Machine Learning"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/SFCS.1986.25"},{"key":"ref34","article-title":"Detecting ai trojans using meta neural analysis","author":"xu","year":"2019"},{"key":"ref10","article-title":"Badnets:Identifying vulnerabilities in the machine learning model supply chain","author":"gu","year":"2017"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref12","article-title":"Learning multiple layers of features from tiny images","author":"krizhevsky","year":"2009"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2020.3021407"},{"key":"ref15","article-title":"Learning to detect malicious clients for robust federated learning","author":"li","year":"2020"},{"key":"ref16","first-page":"6357","article-title":"Ditto:Fair and robust federated learning through personalization","author":"li","year":"2021","journal-title":"International Conference on Machine Learning"},{"key":"ref17","first-page":"519","article-title":"Auc:a statistically consistent and more discriminating measure than accuracy","volume":"3","author":"ling","year":"2003","journal-title":"IJCA"},{"key":"ref18","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","author":"mcmahan","year":"2017","journal-title":"Artificial Intelligence and Statistics"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.12"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2019.00031"},{"key":"ref4","first-page":"634","article-title":"Analyzing federated learning through an adversarial lens","author":"bhagoji","year":"2019","journal-title":"International Conference on Machine Learning"},{"key":"ref27","article-title":"On certifying robustness against backdoor attacks via randomized smoothing","author":"wang","year":"2020"},{"key":"ref3","author":"bernstein","year":"2018","journal-title":"signsgd with majority vote is communication efficient and fault tolerant"},{"key":"ref6","first-page":"1310","author":"cohen","year":"2019","journal-title":"Certified adversarial robustness via randomized smoothing"},{"key":"ref29","article-title":"Attack of the tails:Yes, you really can backdoor federated learning","author":"wang","year":"2020"},{"key":"ref5","first-page":"118","article-title":"Machine learning with adversaries : Byzantine tolerant gradient descent","author":"blanchard","year":"2017","journal-title":"Proceedings of the 31st International Conference on Neural Information Processing Systems"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1145\/1536414.1536440"},{"key":"ref7","author":"data","year":"2020","journal-title":"Byzantine-resilient sgd in high dimensions on heterogeneous data"},{"key":"ref2","first-page":"2938","author":"bagdasaryan","year":"2020","journal-title":"How to backdoor federated learning"},{"key":"ref9","first-page":"201","article-title":"Cryptonets:Applying neural networks to encrypted data with high throughput and accuracy","author":"gilad-bachrach","year":"2016","journal-title":"Int'l Conference on Machine Learning"},{"key":"ref1","first-page":"455","article-title":"Contra:Defending against poisoning attacks in federated learning","author":"awan","year":"2021","journal-title":"Proceedings of European Symposium on Research in Computer Security"},{"key":"ref20","article-title":"Reading digits in natural images with unsupervised feature learning","author":"netzer","year":"2011"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-020-00323-1"},{"key":"ref21","article-title":"Robust aggregation for federated learning","author":"pillutla","year":"2019"},{"key":"ref24","article-title":"Federated multi-task learning","author":"smith","year":"2017"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-69250-1"},{"key":"ref26","article-title":"Slalom:Fast, verifiable and private execution of neural networks in trusted hardware","author":"tramer","year":"2018"},{"key":"ref25","article-title":"Can you really backdoor federated learning?","author":"sun","year":"2019"}],"event":{"name":"2022 26th International Conference on Pattern Recognition (ICPR)","location":"Montreal, QC, Canada","start":{"date-parts":[[2022,8,21]]},"end":{"date-parts":[[2022,8,25]]}},"container-title":["2022 26th International Conference on Pattern Recognition (ICPR)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9956007\/9955631\/09956655.pdf?arnumber=9956655","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,19]],"date-time":"2022-12-19T20:06:51Z","timestamp":1671480411000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9956655\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,21]]},"references-count":37,"URL":"https:\/\/doi.org\/10.1109\/icpr56361.2022.9956655","relation":{},"subject":[],"published":{"date-parts":[[2022,8,21]]}}}