{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,31]],"date-time":"2025-03-31T01:40:08Z","timestamp":1743385208872,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":37,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819628636","type":"print"},{"value":"9789819628643","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[[2025]]},"DOI":"10.1007\/978-981-96-2864-3_9","type":"book-chapter","created":{"date-parts":[[2025,3,31]],"date-time":"2025-03-31T01:16:13Z","timestamp":1743383773000},"page":"103-114","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Efficient Federated Meta Unlearning Algorithm with\u00a0Enhanced Privacy Protection"],"prefix":"10.1007","author":[{"given":"Yani","family":"Wang","sequence":"first","affiliation":[]},{"given":"Zuobin","family":"Ying","sequence":"additional","affiliation":[]},{"given":"Zijie","family":"Pan","sequence":"additional","affiliation":[]},{"given":"Enmin","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Wanlei","family":"Zhou","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,3,29]]},"reference":[{"issue":"11","key":"9_CR1","doi-asserted-by":"publisher","first-page":"583","DOI":"10.3390\/E19110583","volume":"19","author":"M Blachnik","year":"2017","unstructured":"Blachnik, M.: Instance selection for classifier performance estimation in meta learning. Entropy 19(11), 583 (2017). https:\/\/doi.org\/10.3390\/E19110583","journal-title":"Entropy"},{"key":"9_CR2","doi-asserted-by":"publisher","unstructured":"Bollegala, D., O\u2019Neill, J.: A survey on word meta-embedding learning. In: Raedt, L.D. (ed.) Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI 2022, Vienna, Austria, 23\u201329 July 2022, pp. 5402\u20135409. ijcai.org (2022). https:\/\/doi.org\/10.24963\/IJCAI.2022\/758","DOI":"10.24963\/IJCAI.2022\/758"},{"key":"9_CR3","doi-asserted-by":"publisher","unstructured":"Bourtoule, L., et al.: Machine unlearning. In: 42nd IEEE Symposium on Security and Privacy, SP 2021, San Francisco, CA, USA, 24\u201327 May 2021, pp. 141\u2013159. IEEE (2021). https:\/\/doi.org\/10.1109\/SP40001.2021.00019","DOI":"10.1109\/SP40001.2021.00019"},{"key":"9_CR4","doi-asserted-by":"publisher","unstructured":"Cao, Y., Yang, J.: Towards making systems forget with machine unlearning. In: 2015 IEEE Symposium on Security and Privacy, SP 2015, San Jose, CA, USA, 17\u201321 May 2015, pp. 463\u2013480. IEEE Computer Society (2015). https:\/\/doi.org\/10.1109\/SP.2015.35","DOI":"10.1109\/SP.2015.35"},{"key":"9_CR5","unstructured":"Che, T., et al.: Fast federated machine unlearning with nonlinear functional theory. In: Krause, A., Brunskill, E., Cho, K., Engelhardt, B., Sabato, S., Scarlett, J. (eds.) International Conference on Machine Learning, ICML 2023, 23\u201329 July 2023, Honolulu, Hawaii, USA. Proceedings of Machine Learning Research, vol.\u00a0202, pp. 4241\u20134268. PMLR (2023). https:\/\/proceedings.mlr.press\/v202\/che23b.html"},{"key":"9_CR6","unstructured":"Chen, L., Lu, S., Chen, T.: Understanding benign overfitting in gradient-based meta learning. In: Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K., Oh, A. (eds.) Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, NeurIPS 2022, New Orleans, LA, USA, 28 November\u20139 December 2022 (2022). http:\/\/papers.nips.cc\/paper_files\/paper\/2022\/hash\/7db3470825421b6a7e52d95fb00de62e-Abstract-Conference.html"},{"key":"9_CR7","unstructured":"Chen, R., et al.: Fast model debias with machine unlearning. In: Oh, A., Naumann, T., Globerson, A., Saenko, K., Hardt, M., Levine, S. (eds.) Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, 10\u201316 December 2023 (2023). http:\/\/papers.nips.cc\/paper_files\/paper\/2023\/hash\/2ecc80084c96cc25b11b0ab995c25f47-Abstract-Conference.html"},{"key":"9_CR8","unstructured":"Contardo, G., Denoyer, L., Arti\u00e8res, T.: A meta-learning approach to one-step active learning. CoRR abs\/1706.08334 (2017). http:\/\/arxiv.org\/abs\/1706.08334"},{"key":"9_CR9","unstructured":"Finn, C., Abbeel, P., Levine, S.: Model-agnostic meta-learning for fast adaptation of deep networks. In: Precup, D., Teh, Y.W. (eds.) Proceedings of the 34th International Conference on Machine Learning, ICML 2017, Sydney, NSW, Australia, 6\u201311 August 2017. Proceedings of Machine Learning Research, vol.\u00a070, pp. 1126\u20131135. PMLR (2017). http:\/\/proceedings.mlr.press\/v70\/finn17a.html"},{"key":"9_CR10","unstructured":"Finn, C., Rajeswaran, A., Kakade, S.M., Levine, S.: Online meta-learning. In: Chaudhuri, K., Salakhutdinov, R. (eds.) Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9\u201315 June 2019, Long Beach, California, USA. Proceedings of Machine Learning Research, vol.\u00a097, pp. 1920\u20131930. PMLR (2019). http:\/\/proceedings.mlr.press\/v97\/finn19a.html"},{"key":"9_CR11","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1109\/TIFS.2019.2949425","volume":"15","author":"LG Hafemann","year":"2020","unstructured":"Hafemann, L.G., Sabourin, R., Oliveira, L.S.: Meta-learning for fast classifier adaptation to new users of signature verification systems. IEEE Trans. Inf. Forensics Secur. 15, 1735\u20131745 (2020). https:\/\/doi.org\/10.1109\/TIFS.2019.2949425","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"issue":"5","key":"9_CR12","doi-asserted-by":"publisher","first-page":"6703","DOI":"10.1109\/TNNLS.2022.3212627","volume":"35","author":"NM Jebreel","year":"2024","unstructured":"Jebreel, N.M., Domingo-Ferrer, J., Blanco-Justicia, A., S\u00e1nchez, D.: Enhanced security and privacy via fragmented federated learning. IEEE Trans. Neural Netw. Learn. Syst. 35(5), 6703\u20136717 (2024). https:\/\/doi.org\/10.1109\/TNNLS.2022.3212627","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"9_CR13","unstructured":"Jia, J., et al.: Model sparsity can simplify machine unlearning. In: Oh, A., Naumann, T., Globerson, A., Saenko, K., Hardt, M., Levine, S. (eds.) Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, 10\u201316 December 2023 (2023). http:\/\/papers.nips.cc\/paper_files\/paper\/2023\/hash\/a204aa68ab4e970e1ceccfb5b5cdc5e4-Abstract-Conference.html"},{"key":"9_CR14","doi-asserted-by":"publisher","unstructured":"Jiang, H., Zhou, J., Stewart, A., Wang, H.: A short survey on the user cold start problem in recommender systems: metadata and meta-learning methods. In: Tsumoto, S., et al. (eds.) IEEE International Conference on Big Data, Big Data 2022, Osaka, Japan, 17\u201320 December 2022, pp. 3928\u20133934. IEEE (2022). https:\/\/doi.org\/10.1109\/BIGDATA55660.2022.10020294","DOI":"10.1109\/BIGDATA55660.2022.10020294"},{"issue":"4","key":"9_CR15","doi-asserted-by":"publisher","first-page":"5103","DOI":"10.1109\/TNNLS.2022.3202571","volume":"35","author":"X Jiang","year":"2024","unstructured":"Jiang, X., et al.: Deep metric learning based on meta-mining strategy with semiglobal information. IEEE Trans. Neural Netw. Learn. Syst. 35(4), 5103\u20135116 (2024). https:\/\/doi.org\/10.1109\/TNNLS.2022.3202571","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"9_CR16","doi-asserted-by":"crossref","unstructured":"Lake, B.M., Salakhutdinov, R., Tenenbaum, J.B.: The omniglot challenge: a 3-year progress report. CoRR abs\/1902.03477 (2019). http:\/\/arxiv.org\/abs\/1902.03477","DOI":"10.1016\/j.cobeha.2019.04.007"},{"key":"9_CR17","doi-asserted-by":"publisher","unstructured":"Le, D., Thai, M., Nguyen, T.: Multi-task learning for metaphor detection with graph convolutional neural networks and word sense disambiguation. In: The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, The Thirty-Second Innovative Applications of Artificial Intelligence Conference, IAAI 2020, The Tenth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2020, New York, NY, USA, 7\u201312 February 2020, pp. 8139\u20138146. AAAI Press (2020). https:\/\/doi.org\/10.1609\/AAAI.V34I05.6326","DOI":"10.1609\/AAAI.V34I05.6326"},{"key":"9_CR18","doi-asserted-by":"publisher","unstructured":"Lee, H., Li, S., Vu, T.: Meta learning for natural language processing: a survey. In: Carpuat, M., de\u00a0Marneffe, M., Ru\u00edz, I.V.M. (eds.) Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2022, Seattle, WA, USA, 10\u201315 July 2022, pp. 666\u2013684. Association for Computational Linguistics (2022). https:\/\/doi.org\/10.18653\/V1\/2022.NAACL-MAIN.49","DOI":"10.18653\/V1\/2022.NAACL-MAIN.49"},{"key":"9_CR19","unstructured":"Lee, S., Cho, M., Sung, Y.: Parameterizing non-parametric meta-reinforcement learning tasks via subtask decomposition. In: Oh, A., Naumann, T., Globerson, A., Saenko, K., Hardt, M., Levine, S. (eds.) Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, 10\u201316 December 2023 (2023). http:\/\/papers.nips.cc\/paper_files\/paper\/2023\/hash\/86c1fd74fa25bd6be0072937803e0bd1-Abstract-Conference.html"},{"key":"9_CR20","doi-asserted-by":"publisher","first-page":"102718","DOI":"10.1016\/J.ARTMED.2023.102718","volume":"147","author":"Q Liu","year":"2024","unstructured":"Liu, Q., et al.: A few-shot disease diagnosis decision making model based on meta-learning for general practice. Artif. Intell. Med. 147, 102718 (2024). https:\/\/doi.org\/10.1016\/J.ARTMED.2023.102718","journal-title":"Artif. Intell. Med."},{"key":"9_CR21","doi-asserted-by":"publisher","unstructured":"Lu, Q., Zhang, R., Zhou, H., Ni, D., Xiao, W., Li, J.: MetaHMEI: meta-learning for prediction of few-shot histone modifying enzyme inhibitors. Briefings Bioinform. 24(3) (2023). https:\/\/doi.org\/10.1093\/BIB\/BBAD115","DOI":"10.1093\/BIB\/BBAD115"},{"key":"9_CR22","doi-asserted-by":"publisher","unstructured":"Nguyen, Q.P., Oikawa, R., Divakaran, D.M., Chan, M.C., Low, B.K.H.: Markov chain Monte Carlo-based machine unlearning: unlearning what needs to be forgotten. In: Suga, Y., Sakurai, K., Ding, X., Sako, K. (eds.) ASIA CCS 2022: ACM Asia Conference on Computer and Communications Security, Nagasaki, Japan, 30 May\u20133 June 2022. pp. 351\u2013363. ACM (2022). https:\/\/doi.org\/10.1145\/3488932.3517406","DOI":"10.1145\/3488932.3517406"},{"key":"9_CR23","unstructured":"Nichol, A., Achiam, J., Schulman, J.: On first-order meta-learning algorithms. CoRR abs\/1803.02999 (2018). http:\/\/arxiv.org\/abs\/1803.02999"},{"key":"9_CR24","unstructured":"Oreshkin, B.N., L\u00f3pez, P.R., Lacoste, A.: TADAM: task dependent adaptive metric for improved few-shot learning. In: Bengio, S., Wallach, H.M., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, 3\u20138 December 2018, Montr\u00e9al, Canada, pp. 719\u2013729 (2018). https:\/\/proceedings.neurips.cc\/paper\/2018\/hash\/66808e327dc79d135ba18e051673d906-Abstract.html"},{"issue":"3","key":"9_CR25","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1109\/MC.2023.3333319","volume":"57","author":"Y Qu","year":"2024","unstructured":"Qu, Y., Yuan, X., Ding, M., Ni, W., Rakotoarivelo, T., Smith, D.B.: Learn to unlearn: insights into machine unlearning. Computer 57(3), 79\u201390 (2024). https:\/\/doi.org\/10.1109\/MC.2023.3333319","journal-title":"Computer"},{"key":"9_CR26","doi-asserted-by":"publisher","unstructured":"Rotondi, D., Saltarella, M., Giordano, G., Pellecchia, F.: Distributed ledger technology and European union general data protection regulation compliance in a flexible working context. Internet Technol. Lett. 2(5) (2019). https:\/\/doi.org\/10.1002\/ITL2.127","DOI":"10.1002\/ITL2.127"},{"key":"9_CR27","doi-asserted-by":"publisher","unstructured":"Singh, A., Chandrasekar, S., Saha, S., Sen, T.: Federated meta-learning for emotion and sentiment aware multi-modal complaint identification. In: Bouamor, H., Pino, J., Bali, K. (eds.) Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, EMNLP 2023, Singapore, 6\u201310 December 2023, pp. 16091\u201316103. Association for Computational Linguistics (2023). https:\/\/doi.org\/10.18653\/V1\/2023.EMNLP-MAIN.999","DOI":"10.18653\/V1\/2023.EMNLP-MAIN.999"},{"key":"9_CR28","doi-asserted-by":"publisher","unstructured":"Thudi, A., Deza, G., Chandrasekaran, V., Papernot, N.: Unrolling SGD: understanding factors influencing machine unlearning. In: 7th IEEE European Symposium on Security and Privacy, EuroS &P 2022, Genoa, Italy, 6\u201310 June 2022, pp. 303\u2013319. IEEE (2022). https:\/\/doi.org\/10.1109\/EUROSP53844.2022.00027","DOI":"10.1109\/EUROSP53844.2022.00027"},{"key":"9_CR29","unstructured":"Thudi, A., Jia, H., Shumailov, I., Papernot, N.: On the necessity of auditable algorithmic definitions for machine unlearning. In: Butler, K.R.B., Thomas, K. (eds.) 31st USENIX Security Symposium, USENIX Security 2022, Boston, MA, USA, 10\u201312 August 2022, pp. 4007\u20134022. USENIX Association (2022). https:\/\/www.usenix.org\/conference\/usenixsecurity22\/presentation\/thudi"},{"key":"9_CR30","doi-asserted-by":"publisher","unstructured":"Vettoruzzo, A., Bouguelia, M., Vanschoren, J., R\u00f6gnvaldsson, T.S., Santosh, K.: Advances and challenges in meta-learning: a technical review. CoRR abs\/2307.04722 (2023). https:\/\/doi.org\/10.48550\/ARXIV.2307.04722","DOI":"10.48550\/ARXIV.2307.04722"},{"key":"9_CR31","doi-asserted-by":"publisher","unstructured":"Vilalta, R., Drissi, Y.: A perspective view and survey of meta-learning. Artif. Intell. Rev. 18(2), 77\u201395 (2002). https:\/\/doi.org\/10.1023\/A:1019956318069","DOI":"10.1023\/A:1019956318069"},{"key":"9_CR32","doi-asserted-by":"crossref","unstructured":"Warnecke, A., Pirch, L., Wressnegger, C., Rieck, K.: Machine unlearning of features and labels. In: 30th Annual Network and Distributed System Security Symposium, NDSS 2023, San Diego, California, USA, 27 February\u20133 March 2023. The Internet Society (2023). https:\/\/www.ndss-symposium.org\/ndss-paper\/machine-unlearning-of-features-and-labels\/","DOI":"10.14722\/ndss.2023.23087"},{"key":"9_CR33","doi-asserted-by":"publisher","unstructured":"Xu, H., Zhu, T., Zhang, L., Zhou, W., Yu, P.S.: Machine unlearning: a survey. ACM Comput. Surv. 56(1), 9:1\u20139:36 (2024). https:\/\/doi.org\/10.1145\/3603620","DOI":"10.1145\/3603620"},{"key":"9_CR34","doi-asserted-by":"publisher","unstructured":"Yang, J., Wang, X., Luo, Z.: Few-shot remaining useful life prediction based on meta-learning with deep sparse kernel network. Inf. Sci. 653, 119795 (2024). https:\/\/doi.org\/10.1016\/J.INS.2023.119795","DOI":"10.1016\/J.INS.2023.119795"},{"key":"9_CR35","doi-asserted-by":"publisher","unstructured":"Yang, L., Huang, J., Lin, W., Cao, J.: Personalized federated learning on non-IID data via group-based meta-learning. ACM Trans. Knowl. Discov. Data 17(4), 49:1\u201349:20 (2023). https:\/\/doi.org\/10.1145\/3558005","DOI":"10.1145\/3558005"},{"key":"9_CR36","doi-asserted-by":"publisher","unstructured":"Zhang, P., Bai, G., Huang, Z., Xu, X.: Machine unlearning for image retrieval: a generative scrubbing approach. In: Magalh\u00e3es, J., et al. (eds.) MM 2022: The 30th ACM International Conference on Multimedia, Lisboa, Portugal, 10\u201314 October 2022, pp. 237\u2013245. ACM (2022). https:\/\/doi.org\/10.1145\/3503161.3548378","DOI":"10.1145\/3503161.3548378"},{"key":"9_CR37","doi-asserted-by":"publisher","unstructured":"Zhang, X., Kang, Y., Fan, L., Chen, K., Yang, Q.: A meta-learning framework for tuning parameters of protection mechanisms in trustworthy federated learning. ACM Trans. Intell. Syst. Technol. 15(3), 55:1\u201355:36 (2024). https:\/\/doi.org\/10.1145\/3652612","DOI":"10.1145\/3652612"}],"container-title":["Lecture Notes in Computer Science","Network and Parallel Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-2864-3_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,31]],"date-time":"2025-03-31T01:16:24Z","timestamp":1743383784000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-2864-3_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819628636","9789819628643"],"references-count":37,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-2864-3_9","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"29 March 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"NPC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"IFIP International Conference on Network and Parallel Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Haikou","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":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 December 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 December 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"npc2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}