{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T00:31:00Z","timestamp":1742949060716,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":32,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819771837"},{"type":"electronic","value":"9789819771844"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-981-97-7184-4_6","type":"book-chapter","created":{"date-parts":[[2024,8,22]],"date-time":"2024-08-22T06:41:16Z","timestamp":1724308876000},"page":"61-71","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Robust Heterogeneous Federated Learning via Data-Free Knowledge Amalgamation"],"prefix":"10.1007","author":[{"given":"Jun","family":"Ma","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zheng","family":"Fan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chaoyu","family":"Fan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qi","family":"Kang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,8,21]]},"reference":[{"key":"6_CR1","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Presented at the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"6_CR2","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Presented at the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015)","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"6_CR3","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems. Curran Associates Inc. (2012)"},{"key":"6_CR4","unstructured":"Kairouz, P., et al.: Advances and open problems in federated learning. Found. Trends\u00ae Mach. Learn. 14, 1\u2013210 (2021)"},{"key":"6_CR5","first-page":"50","volume":"37","author":"T Li","year":"2020","unstructured":"Li, T., Sahu, A.K., Talwalkar, A., Smith, V.: Federated learning: challenges, methods, and future directions. IEEE Signal Process. Mag. 37, 50\u201360 (2020)","journal-title":"IEEE Signal Process. Mag."},{"key":"6_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3339474","volume":"10","author":"Q Yang","year":"2019","unstructured":"Yang, Q., Liu, Y., Chen, T., Tong, Y.: Federated machine learning: concept and applications. ACM Trans. Intell. Syst. Technol. 10, 1\u201319 (2019)","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"6_CR7","doi-asserted-by":"publisher","first-page":"16301","DOI":"10.1109\/JSEN.2021.3076767","volume":"21","author":"R Kumar","year":"2021","unstructured":"Kumar, R., et al.: Blockchain-federated-learning and deep learning models for COVID-19 detection using CT imaging. IEEE Sens. J. 21, 16301\u201316314 (2021)","journal-title":"IEEE Sens. J."},{"key":"6_CR8","doi-asserted-by":"publisher","first-page":"305","DOI":"10.1038\/s42256-020-0186-1","volume":"2","author":"GA Kaissis","year":"2020","unstructured":"Kaissis, G.A., Makowski, M.R., R\u00fcckert, D., Braren, R.F.: Secure, privacy-preserving and federated machine learning in medical imaging. Nat. Mach. Intell. 2, 305\u2013311 (2020)","journal-title":"Nat. Mach. Intell."},{"key":"6_CR9","first-page":"13172","volume":"34","author":"Y Liu","year":"2020","unstructured":"Liu, Y., et al.: FedVision: an online visual object detection platform powered by federated learning. Proc. AAAI Conf. Artif. Intell. 34, 13172\u201313179 (2020)","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"6_CR10","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1007\/978-3-030-58607-2_5","volume-title":"Computer Vision \u2013 ECCV 2020","author":"T-MH Hsu","year":"2020","unstructured":"Hsu, T.-M.H., Qi, H., Brown, M.: Federated visual classification with real-world data distribution. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) Computer Vision \u2013 ECCV 2020, pp. 76\u201392. Springer International Publishing, Cham (2020)"},{"key":"6_CR11","unstructured":"McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Singh, A., Zhu, J. (eds.) Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, pp. 1273\u20131282. PMLR (2017)"},{"key":"6_CR12","first-page":"429","volume":"2","author":"T Li","year":"2020","unstructured":"Li, T., Sahu, A.K., Zaheer, M., Sanjabi, M., Talwalkar, A., Smith, V.: Federated optimization in heterogeneous networks. Proc. Mach. Learn. Syst. 2, 429\u2013450 (2020)","journal-title":"Proc. Mach. Learn. Syst."},{"key":"6_CR13","unstructured":"Karimireddy, S.P., Kale, S., Mohri, M., Reddi, S., Stich, S., Suresh, A.T.: SCAFFOLD: stochastic controlled averaging for federated learning. In: Proceedings of the 37th International Conference on Machine Learning, pp. 5132\u20135143. PMLR (2020)"},{"key":"6_CR14","unstructured":"Wang, H., Yurochkin, M., Sun, Y., Papailiopoulos, D., Khazaeni, Y.: Federated Learning with Matched Averaging (2020). http:\/\/arxiv.org\/abs\/2002.06440"},{"key":"6_CR15","unstructured":"Hsu, T.-M.H., Qi, H., Brown, M.: Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification (2019). http:\/\/arxiv.org\/abs\/1909.06335"},{"key":"6_CR16","doi-asserted-by":"crossref","unstructured":"Li, Q., He, B., Song, D.: Model-contrastive federated learning. In: Presented at the Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2021)","DOI":"10.1109\/CVPR46437.2021.01057"},{"key":"6_CR17","doi-asserted-by":"crossref","unstructured":"Han, S., et al.: FedX: unsupervised federated learning with cross knowledge distillation. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) Computer Vision \u2013 ECCV 2022, pp. 691\u2013707. Springer Nature Switzerland, Cham (2022)","DOI":"10.1007\/978-3-031-20056-4_40"},{"key":"6_CR18","unstructured":"Hu, S., Feng, L., Yang, X., Chen, Y.: FedSSC: Shared Supervised-Contrastive Federated Learning (2023). http:\/\/arxiv.org\/abs\/2301.05797"},{"key":"6_CR19","first-page":"7852","volume":"35","author":"T Qi","year":"2022","unstructured":"Qi, T., et al.: FairVFL: a fair vertical federated learning framework with contrastive adversarial learning. Adv. Neural. Inf. Process. Syst. 35, 7852\u20137865 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"6_CR20","unstructured":"Fang, G., Song, J., Shen, C., Wang, X., Chen, D., Song, M.: Data-Free Adversarial Distillation (2020). http:\/\/arxiv.org\/abs\/1912.11006"},{"key":"6_CR21","doi-asserted-by":"crossref","unstructured":"Chen, H., et al.: Data-free learning of student networks. In: Presented at the Proceedings of the IEEE\/CVF International Conference on Computer Vision (2019)","DOI":"10.1109\/ICCV.2019.00361"},{"key":"6_CR22","doi-asserted-by":"crossref","unstructured":"Fang, G., Song, J., Wang, X., Shen, C., Wang, X., Song, M.: Contrastive Model Inversion for Data-Free Knowledge Distillation (2021). http:\/\/arxiv.org\/abs\/2105.08584","DOI":"10.24963\/ijcai.2021\/327"},{"key":"6_CR23","doi-asserted-by":"crossref","unstructured":"Binici, K., Pham, N.T., Mitra, T., Leman, K.: Preventing catastrophic forgetting and distribution mismatch in knowledge distillation via synthetic data. In: Presented at the Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision (2022)","DOI":"10.1109\/WACV51458.2022.00368"},{"key":"6_CR24","unstructured":"Nayak, G.K., Mopuri, K.R., Shaj, V., Radhakrishnan, V.B., Chakraborty, A.: Zero-shot knowledge distillation in deep networks. In: Proceedings of the 36th International Conference on Machine Learning, pp. 4743\u20134751. PMLR (2019)"},{"key":"6_CR25","doi-asserted-by":"crossref","unstructured":"Yin, H., et al.: Dreaming to distill: data-free knowledge transfer via deep inversion. In: Presented at the Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2020)","DOI":"10.1109\/CVPR42600.2020.00874"},{"key":"6_CR26","unstructured":"Zhu, Z., Hong, J., Zhou, J.: Data-free knowledge distillation for heterogeneous federated learning. In: Proceedings of the 38th International Conference on Machine Learning, pp. 12878\u201312889. PMLR (2021)"},{"key":"6_CR27","first-page":"22045","volume":"33","author":"SP Singh","year":"2020","unstructured":"Singh, S.P., Jaggi, M.: Model fusion via optimal transport. Adv. Neural. Inf. Process. Syst. 33, 22045\u201322055 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"6_CR28","doi-asserted-by":"publisher","first-page":"3771","DOI":"10.1609\/aaai.v33i01.33013771","volume":"33","author":"B Heo","year":"2019","unstructured":"Heo, B., Lee, M., Yun, S., Choi, J.Y.: Knowledge distillation with adversarial samples supporting decision boundary. Proc. AAAI Conf. Artif. Intell. 33, 3771\u20133778 (2019). https:\/\/doi.org\/10.1609\/aaai.v33i01.33013771","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"6_CR29","first-page":"2351","volume":"33","author":"T Lin","year":"2020","unstructured":"Lin, T., Kong, L., Stich, S.U., Jaggi, M.: Ensemble distillation for robust model fusion in federated learning. Adv. Neural. Inf. Process. Syst. 33, 2351\u20132363 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"6_CR30","first-page":"16001","volume":"33","author":"S Zhang","year":"2020","unstructured":"Zhang, S., Liu, M., Yan, J.: The diversified ensemble neural network. Adv. Neural. Inf. Process. Syst. 33, 16001\u201316011 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"6_CR31","first-page":"723","volume":"13","author":"A Gretton","year":"2012","unstructured":"Gretton, A., Borgwardt, K.M., Rasch, M.J., Sch\u00f6lkopf, B., Smola, A.: A kernel two-sample test. J. Mach. Learn. Res. 13, 723\u2013773 (2012)","journal-title":"J. Mach. Learn. Res."},{"key":"6_CR32","unstructured":"Paszke, A., et al.: Pytorch: an imperative style, high-performance deep learning library. Adv. Neural Inf. Process. Syst. 32 (2019)"}],"container-title":["Lecture Notes in Computer Science","Advances in Swarm Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-7184-4_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,22]],"date-time":"2024-08-22T06:42:45Z","timestamp":1724308965000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-7184-4_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9789819771837","9789819771844"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-7184-4_6","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"21 August 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICSI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Swarm Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Xining","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":"22 August 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 August 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"swarm2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.iasei.org\/icsi2024\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}