{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T07:02:19Z","timestamp":1780729339574,"version":"3.54.1"},"publisher-location":"Singapore","reference-count":24,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819214617","type":"print"},{"value":"9789819214624","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-981-92-1462-4_15","type":"book-chapter","created":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T06:45:27Z","timestamp":1780728327000},"page":"184-196","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["EarlyShield: Early-Stage Screening for\u00a0Robust Personalized Federated Learning"],"prefix":"10.1007","author":[{"given":"Shixiong","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xingyu","family":"Lyu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ning","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Danjue","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yidan","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yimin","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,7]]},"reference":[{"key":"15_CR1","unstructured":"Bagdasaryan, E., Veit, A., Hua, Y., Estrin, D., Shmatikov, V.: How to backdoor federated learning, pp. 2938\u20132948. AISTATS (2020) PMLR"},{"key":"15_CR2","unstructured":"Wang, H., et al.: Attack of the tails: Yes, you really can backdoor federated learning. NeurIPS 33, 16070\u201316084 (2020)"},{"key":"15_CR3","unstructured":"T\u00a0Dinh, C., Tran, N., Nguyen, J.: Personalized federated learning with moreau envelopes. NeurIPS (2020)"},{"key":"15_CR4","doi-asserted-by":"crossref","unstructured":"Ye, T., Chen, C., Wang, Y., Li, X., Gao, M.: Bapfl: You can backdoor personalized federated learning. TKDD (2024)","DOI":"10.1145\/3649316"},{"key":"15_CR5","unstructured":"Lyu, X., et al.: Lurking in the shadows: Unveiling stealthy backdoor attacks against personalized federated learning. In: USENIX Security (2024)"},{"key":"15_CR6","unstructured":"Fang, M., Cao, X., Jia, J., Gong, N.: Local model poisoning attacks to $$\\{$$Byzantine-Robust$$\\}$$ federated learning. In: USENIX Security (2020)"},{"key":"15_CR7","doi-asserted-by":"publisher","first-page":"47230","DOI":"10.1109\/ACCESS.2019.2909068","volume":"7","author":"T Gu","year":"2019","unstructured":"Gu, T., Liu, K., Dolan-Gavitt, B., Garg, S.: Badnets: evaluating backdooring attacks on deep neural networks. IEEE Access 7, 47230\u201347244 (2019)","journal-title":"IEEE Access"},{"key":"15_CR8","unstructured":"Xie, C., Huang, K., Chen, P.Y., Li, B.: Dba: Distributed backdoor attacks against federated learning. In: ICLR (2019)"},{"key":"15_CR9","doi-asserted-by":"crossref","unstructured":"Bi, Z., Singha, A., Xue, H., Li, T., Chen, Y., Zhang, Y.: Physical backdoor attacks against mmwave-based human activity recognition. In: IEEE ICDCS, pp. 758\u2013768. IEEE (2025)","DOI":"10.1109\/ICDCS63083.2025.00079"},{"key":"15_CR10","doi-asserted-by":"crossref","unstructured":"Li, S., Lyu, X., Wang, N., Li, T., Chen, D., Chen, Y.: Beyond uniformity: Robust backdoor attacks on deep neural networks with trigger selection. In: PAKDD, pp. 290\u2013302. Springer (2025)","DOI":"10.1007\/978-981-96-8295-9_21"},{"key":"15_CR11","doi-asserted-by":"publisher","unstructured":"Jiang, Z., et al.: Boba: Boosting backdoor detection through data distribution inference in federated learning. In: ECAI. Frontiers in Artificial Intelligence and Applications, vol.\u00a0413, pp. 1051\u20131058 (2025). https:\/\/doi.org\/10.3233\/FAIA250914research Article","DOI":"10.3233\/FAIA250914"},{"key":"15_CR12","unstructured":"Yin, D., Chen, Y., Kannan, R., Bartlett, P.: Byzantine-robust distributed learning: Towards optimal statistical rates. In: ICML (2018)"},{"key":"15_CR13","doi-asserted-by":"crossref","unstructured":"Cao, X., Fang, M., Liu, J., Gong, N.Z.: Fltrust: Byzantine-robust federated learning via trust bootstrapping. In: NDSS (2021)","DOI":"10.14722\/ndss.2021.24434"},{"key":"15_CR14","first-page":"1","volume":"01","author":"N Wang","year":"2024","unstructured":"Wang, N., Zhang, C., Xiao, Y., Chen, Y., Lou, W., Hou, Y.T.: Flare: defending federated learning against model poisoning attacks via latent space representations. IEEE TDSC 01, 1\u201317 (2024)","journal-title":"IEEE TDSC"},{"key":"15_CR15","unstructured":"Nguyen, T.D., et\u00a0al.: $$\\{$$FLAME$$\\}$$: Taming backdoors in federated learning. In: USENIX Security (2022)"},{"key":"15_CR16","unstructured":"Lyu, X., et al.: Two heads are better than one: Model-weight and latent-space analysis for federated learning on non-iid data against poisoning attacks. arXiv preprint arXiv:2503.23288 (2025)"},{"key":"15_CR17","doi-asserted-by":"crossref","unstructured":"Lyu, X., et al.: Buffer is all you need: Defending federated learning against backdoor attacks under non-iids via buffering. In: IEEE TrustCom, pp. 236\u2013243. IEEE (2025)","DOI":"10.1109\/Trustcom66490.2025.00034"},{"key":"15_CR18","doi-asserted-by":"crossref","unstructured":"Fan, T., Chen, X., Dong, Y., Chen, X., Xuan, Y., Jing, W.: Lightweight secure aggregation for personalized federated learning with backdoor resistance. In: ACSAC (2024)","DOI":"10.1109\/ACSAC63791.2024.00071"},{"key":"15_CR19","doi-asserted-by":"crossref","unstructured":"Chen, G., et al.: Robustpfl: robust personalized federated learning. In: IEEE TDSC (2025)","DOI":"10.1109\/TDSC.2025.3526840"},{"key":"15_CR20","doi-asserted-by":"crossref","unstructured":"Li, Q., Diao, Y., Chen, Q., He, B.: Federated learning on non-iid data silos: an experimental study. In: IEEE ICDE (2022)","DOI":"10.1109\/ICDE53745.2022.00077"},{"key":"15_CR21","unstructured":"Zhuang, H., Yu, M., Wang, H., Hua, Y., Li, J., Yuan, X.: Backdoor federated learning by poisoning backdoor-critical layers. In: ICLR (2024)"},{"key":"15_CR22","doi-asserted-by":"crossref","unstructured":"Kabir, E., Song, Z., Rashid, M.R.U., Mehnaz, S.: Flshield: a validation based federated learning framework to defend against poisoning attacks. In: IEEE S&P (2024)","DOI":"10.1109\/SP54263.2024.00141"},{"key":"15_CR23","unstructured":"Li, T., Sahu, A.K., Zaheer, M., Sanjabi, M., Talwalkar, A., Smith, V.: Federated optimization in heterogeneous networks. In: MLSys (2020)"},{"key":"15_CR24","unstructured":"Wang, K., Mathews, R., Kiddon, C., Eichner, H., Beaufays, F., Ramage, D.: Federated evaluation of on-device personalization, (2019). arXiv:1910.10252 arXiv preprint"}],"container-title":["Lecture Notes in Computer Science","Advances in Knowledge Discovery and Data Mining"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-92-1462-4_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T06:45:32Z","timestamp":1780728332000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-92-1462-4_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819214617","9789819214624"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-981-92-1462-4_15","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"7 June 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PAKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pacific-Asia Conference on Knowledge Discovery and Data Mining","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hong Kong","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 June 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 June 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pakdd2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.pakdd2026.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}