{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T15:24:58Z","timestamp":1775229898194,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":66,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,8,14]],"date-time":"2022-08-14T00:00:00Z","timestamp":1660435200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,8,14]]},"DOI":"10.1145\/3534678.3539174","type":"proceedings-article","created":{"date-parts":[[2022,8,12]],"date-time":"2022-08-12T19:06:41Z","timestamp":1660331201000},"page":"2780-2788","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["ILASR: Privacy-Preserving Incremental Learning for Automatic Speech Recognition at Production Scale"],"prefix":"10.1145","author":[{"given":"Gopinath","family":"Chennupati","sequence":"first","affiliation":[{"name":"Amazon Alexa, Sunnyvale, CA, USA"}]},{"given":"Milind","family":"Rao","sequence":"additional","affiliation":[{"name":"Amazon Alexa, Sunnyvale, CA, USA"}]},{"given":"Gurpreet","family":"Chadha","sequence":"additional","affiliation":[{"name":"Amazon Alexa, Seattle, WA, USA"}]},{"given":"Aaron","family":"Eakin","sequence":"additional","affiliation":[{"name":"Amazon Alexa, Seattle, WA, USA"}]},{"given":"Anirudh","family":"Raju","sequence":"additional","affiliation":[{"name":"Amazon Alexa, Seattle, CA, USA"}]},{"given":"Gautam","family":"Tiwari","sequence":"additional","affiliation":[{"name":"Amazon Alexa, Sunnyvale, CA, USA"}]},{"given":"Anit Kumar","family":"Sahu","sequence":"additional","affiliation":[{"name":"Amazon Alexa, Seattle, WA, USA"}]},{"given":"Ariya","family":"Rastrow","sequence":"additional","affiliation":[{"name":"Amazon Alexa, Seattle, WA, USA"}]},{"given":"Jasha","family":"Droppo","sequence":"additional","affiliation":[{"name":"Amazon Alexa, Seattle, WA, USA"}]},{"given":"Andy","family":"Oberlin","sequence":"additional","affiliation":[{"name":"Amazon Alexa, Seattle, WA, USA"}]},{"given":"Buddha","family":"Nandanoor","sequence":"additional","affiliation":[{"name":"Amazon Alexa, Sunnyvale, CA, USA"}]},{"given":"Prahalad","family":"Venkataramanan","sequence":"additional","affiliation":[{"name":"Amazon Alexa, Seattle, WA, USA"}]},{"given":"Zheng","family":"Wu","sequence":"additional","affiliation":[{"name":"Amazon Alexa, Seattle, WA, USA"}]},{"given":"Pankaj","family":"Sitpure","sequence":"additional","affiliation":[{"name":"Amazon Alexa, Sunnyvale, CA, USA"}]}],"member":"320","published-online":{"date-parts":[[2022,8,14]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/MSEC.2018.2888775"},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1080\/09540099550039318"},{"key":"e_1_3_2_2_3_1","first-page":"12449","article-title":"wav2vec 2.0: A framework for self-supervised learning of speech representations","volume":"33","author":"Baevski A.","year":"2020","unstructured":"Baevski, A., Zhou, Y., Mohamed, A., and Auli, M. wav2vec 2.0: A framework for self-supervised learning of speech representations. Advances in Neural Information Processing Systems, 33: 12449--12460, 2020.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_4_1","volume-title":"Data2vec: A general framework for self-supervised learning in speech, vision and language. arXiv preprint arXiv:2202.03555","author":"Baevski A.","year":"2022","unstructured":"Baevski, A., Hsu, W.-N., Xu, Q., Babu, A., Gu, J., and Auli, M. Data2vec: A general framework for self-supervised learning in speech, vision and language. arXiv preprint arXiv:2202.03555, 2022."},{"key":"e_1_3_2_2_5_1","unstructured":"Mazzocchi McMahan et al.]bonawitz2019towardsBonawitz K. Eichner H. Grieskamp W. Huba D. Ingerman A. Ivanov V. Kiddon C. Konevc n? J. Mazzocchi S. McMahan H. B. et al. Towards federated learning at scale: System design. arXiv preprint arXiv:1902.01046 2019."},{"key":"e_1_3_2_2_6_1","volume-title":"Connectionist speech recognition: a hybrid approach","author":"Bourlard H. A.","year":"2012","unstructured":"Bourlard, H. A. and Morgan, N. Connectionist speech recognition: a hybrid approach, volume 247. Springer Science & Business Media, 2012."},{"key":"e_1_3_2_2_7_1","first-page":"11285","article-title":"Tinytl: Reduce memory, not parameters for efficient on-device learning","volume":"33","author":"Cai H.","year":"2020","unstructured":"Cai, H., Gan, C., Zhu, L., and Han, S. Tinytl: Reduce memory, not parameters for efficient on-device learning. Advances in Neural Information Processing Systems, 33: 11285--11297, 2020.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_8_1","first-page":"233","volume-title":"Proceedings of the European conference on computer vision (ECCV)","author":"Jim\u00e9nez","year":"2018","unstructured":"Jim\u00e9nez, Guil, Schmid, and Alahari]castro2018endCastro, F. M., Mar'in-Jim\u00e9nez, M. J., Guil, N., Schmid, C., and Alahari, K. End-to-end incremental learning. In Proceedings of the European conference on computer vision (ECCV), pp. 233--248, 2018."},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2016.7472805"},{"key":"e_1_3_2_2_10_1","volume-title":"Wavlm: Large-scale self-supervised pre-training for full stack speech processing. arXiv preprint arXiv:2110.13900","author":"Chen S.","year":"2021","unstructured":"Chen, S., Wang, C., Chen, Z., Wu, Y., Liu, S., Chen, Z., Li, J., Kanda, N., Yoshioka, T., Xiao, X., et al. Wavlm: Large-scale self-supervised pre-training for full stack speech processing. arXiv preprint arXiv:2110.13900, 2021."},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2018.8462105"},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP40776.2020.9054438"},{"key":"e_1_3_2_2_13_1","volume-title":"Federated acoustic modeling for automatic speech recognition. CoRR, abs\/2102.04429","author":"Cui X.","year":"2021","unstructured":"Cui, X., Lu, S., and Kingsbury, B. Federated acoustic modeling for automatic speech recognition. CoRR, abs\/2102.04429, 2021."},{"key":"e_1_3_2_2_14_1","volume-title":"Exploiting semi-supervised training through a dropout regularization in end-to-end speech recognition. arXiv preprint arXiv:1908.05227","author":"Dey S.","year":"2019","unstructured":"Dey, S., Motlicek, P., Bui, T., and Dernoncourt, F. Exploiting semi-supervised training through a dropout regularization in end-to-end speech recognition. arXiv preprint arXiv:1908.05227, 2019."},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.17487\/rfc5246"},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.21437\/Interspeech.2020-1791"},{"key":"e_1_3_2_2_17_1","volume-title":"Catastrophic forgetting in connectionist networks. Trends in cognitive sciences, 3 (4): 128--135","author":"French R. M.","year":"1999","unstructured":"French, R. M. Catastrophic forgetting in connectionist networks. Trends in cognitive sciences, 3 (4): 128--135, 1999."},{"key":"e_1_3_2_2_18_1","volume-title":"End-to-end speech recognition from federated acoustic models","author":"Gao Y.","year":"2021","unstructured":"Gao, Y., Parcollet, T., Fernandez-Marques, J., de Gusmao, P. P. B., Beutel, D. J., and Lane, N. D. End-to-end speech recognition from federated acoustic models, 2021."},{"key":"e_1_3_2_2_19_1","volume-title":"Differentially private federated learning: A client level perspective. arXiv preprint arXiv:1712.07557","author":"Geyer R. C.","year":"2017","unstructured":"Geyer, R. C., Klein, T., and Nabi, M. Differentially private federated learning: A client level perspective. arXiv preprint arXiv:1712.07557, 2017."},{"key":"e_1_3_2_2_20_1","volume-title":"On the computational inefficiency of large batch sizes for stochastic gradient descent. arXiv preprint arXiv:1811.12941","author":"Golmant N.","year":"2018","unstructured":"Golmant, N., Vemuri, N., Yao, Z., Feinberg, V., Gholami, A., Rothauge, K., Mahoney, M. W., and Gonzalez, J. On the computational inefficiency of large batch sizes for stochastic gradient descent. arXiv preprint arXiv:1811.12941, 2018."},{"key":"e_1_3_2_2_21_1","volume-title":"An empirical investigation of catastrophic forgetting in gradient-based neural networks. arXiv preprint arXiv:1312.6211","author":"Goodfellow I. J.","year":"2013","unstructured":"Goodfellow, I. J., Mirza, M., Xiao, D., Courville, A., and Bengio, Y. An empirical investigation of catastrophic forgetting in gradient-based neural networks. arXiv preprint arXiv:1312.6211, 2013."},{"key":"e_1_3_2_2_22_1","volume-title":"Accurate, large minibatch sgd: Training imagenet in 1 hour. arXiv preprint arXiv:1706.02677","author":"Girshick","year":"2017","unstructured":"Girshick, Noordhuis, Wesolowski, Kyrola, Tulloch, Jia, and He]goyal2017accurateGoyal, P., Doll\u00e1r, P., Girshick, R., Noordhuis, P., Wesolowski, L., Kyrola, A., Tulloch, A., Jia, Y., and He, K. Accurate, large minibatch sgd: Training imagenet in 1 hour. arXiv preprint arXiv:1706.02677, 2017."},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.21437\/Interspeech.2020-2944"},{"key":"e_1_3_2_2_24_1","volume-title":"Sequence transduction with recurrent neural networks. arXiv preprint arXiv:1211.3711","author":"Graves A.","year":"2012","unstructured":"Graves, A. Sequence transduction with recurrent neural networks. arXiv preprint arXiv:1211.3711, 2012."},{"key":"e_1_3_2_2_25_1","volume-title":"Conformer: Convolution-augmented transformer for speech recognition. arXiv preprint arXiv:2005.08100","author":"Gulati A.","year":"2020","unstructured":"Gulati, A., Qin, J., Chiu, C.-C., Parmar, N., Zhang, Y., Yu, J., Han, W., Wang, S., Zhang, Z., Wu, Y., et al. Conformer: Convolution-augmented transformer for speech recognition. arXiv preprint arXiv:2005.08100, 2020."},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP39728.2021.9413397"},{"key":"e_1_3_2_2_27_1","volume-title":"Train longer, generalize better: closing the generalization gap in large batch training of neural networks. arXiv preprint arXiv:1705.08741","author":"Hoffer E.","year":"2017","unstructured":"Hoffer, E., Hubara, I., and Soudry, D. Train longer, generalize better: closing the generalization gap in large batch training of neural networks. arXiv preprint arXiv:1705.08741, 2017."},{"key":"e_1_3_2_2_28_1","volume-title":"Hubert: Self-supervised speech representation learning by masked prediction of hidden units","author":"Hsu W.-N.","year":"2021","unstructured":"Hsu, W.-N., Bolte, B., Tsai, Y.-H. H., Lakhotia, K., Salakhutdinov, R., and Mohamed, A. Hubert: Self-supervised speech representation learning by masked prediction of hidden units. IEEE\/ACM Transactions on Audio, Speech, and Language Processing, 29: 3451--3460, 2021."},{"key":"e_1_3_2_2_29_1","volume-title":"Confidence measures for speech recognition: A survey. Speech communication, 45 (4): 455--470","author":"Jiang H.","year":"2005","unstructured":"Jiang, H. Confidence measures for speech recognition: A survey. Speech communication, 45 (4): 455--470, 2005."},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2015.7178922"},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.21437\/Interspeech.2018-1746"},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2019.8682890"},{"key":"e_1_3_2_2_33_1","volume-title":"On large-batch training for deep learning: Generalization gap and sharp minima. arXiv preprint arXiv:1609.04836","author":"Keskar N. S.","year":"2016","unstructured":"Keskar, N. S., Mudigere, D., Nocedal, J., Smelyanskiy, M., and Tang, P. T. P. On large-batch training for deep learning: Generalization gap and sharp minima. arXiv preprint arXiv:1609.04836, 2016."},{"key":"e_1_3_2_2_34_1","volume-title":"Int. Workshop on Federated Learning for User Privacy and Data Confidentiality in Conjunction with ICML 2020","author":"Khodak M.","year":"2020","unstructured":"Khodak, M., Li, T., Li, L., Balcan, M., Smith, V., and Talwalkar, A. Weight sharing for hyperparameter optimization in federated learning. In Int. Workshop on Federated Learning for User Privacy and Data Confidentiality in Conjunction with ICML 2020, 2020."},{"key":"e_1_3_2_2_35_1","volume-title":"ACL","author":"Kudo T.","year":"2018","unstructured":"Kudo, T. Subword regularization: Improving neural network translation models with multiple subword candidates. In ACL, 2018."},{"key":"e_1_3_2_2_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2019.8682172"},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623612"},{"key":"e_1_3_2_2_38_1","volume-title":"Don't use large mini-batches, use local sgd. ArXiv, abs\/1808.07217","author":"Lin T.","year":"2020","unstructured":"Lin, T., Stich, S. U., and Jaggi, M. Don't use large mini-batches, use local sgd. ArXiv, abs\/1808.07217, 2020."},{"key":"e_1_3_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP40776.2020.9053176"},{"key":"e_1_3_2_2_40_1","volume-title":"Global Journal of Computer Science and Technology","author":"Mahajan P.","year":"2013","unstructured":"Mahajan, P. and Sachdeva, A. A study of encryption algorithms aes, des and rsa for security. Global Journal of Computer Science and Technology, 2013."},{"key":"e_1_3_2_2_41_1","volume-title":"Revisiting small batch training for deep neural networks. arXiv preprint arXiv:1804.07612","author":"Masters D.","year":"2018","unstructured":"Masters, D. and Luschi, C. Revisiting small batch training for deep neural networks. arXiv preprint arXiv:1804.07612, 2018."},{"key":"e_1_3_2_2_42_1","volume-title":"An empirical model of large-batch training. arXiv preprint arXiv:1812.06162","author":"McCandlish S.","year":"2018","unstructured":"McCandlish, S., Kaplan, J., Amodei, D., and Team, O. D. An empirical model of large-batch training. arXiv preprint arXiv:1812.06162, 2018."},{"key":"e_1_3_2_2_43_1","first-page":"109","volume-title":"Psychology of learning and motivation","author":"McCloskey M.","year":"1989","unstructured":"McCloskey, M. and Cohen, N. J. Catastrophic interference in connectionist networks: The sequential learning problem. In Psychology of learning and motivation, volume 24, pp. 109--165. Elsevier, 1989."},{"key":"e_1_3_2_2_44_1","first-page":"1273","volume-title":"Artificial Intelligence and Statistics","author":"McMahan B.","year":"2017","unstructured":"McMahan, B., Moore, E., Ramage, D., Hampson, S., and y Arcas, B. A. Communication-efficient learning of deep networks from decentralized data. In Artificial Intelligence and Statistics, pp. 1273--1282. PMLR, 2017."},{"key":"e_1_3_2_2_45_1","volume-title":"Self-supervised speech representation learning: A review. arXiv preprint arXiv:2205.10643","author":"Mohamed A.","year":"2022","unstructured":"et al.]mohamed2022selfMohamed, A., Lee, H.-y., Borgholt, L., Havtorn, J. D., Edin, J., Igel, C., Kirchhoff, K., Li, S.-W., Livescu, K., Maal\u00f8e, L., et al. Self-supervised speech representation learning: A review. arXiv preprint arXiv:2205.10643, 2022."},{"key":"e_1_3_2_2_46_1","first-page":"1381","volume-title":"EUROSPEECH'95: 4th European Conference on Speech Communication and Technology: Madrid, Spain: 18--21","author":"Nadeu Camprub'i","year":"1995","unstructured":"t al.(1995)Nadeu Camprub'i, Hernando Peric\u00e1s, and Gorricho Moreno]nadeu1995decorrelationNadeu Camprub'i, C., Hernando Peric\u00e1s, F. J., and Gorricho Moreno, M. On the decorrelation of filter-bank energies in speech recognition. In EUROSPEECH'95: 4th European Conference on Speech Communication and Technology: Madrid, Spain: 18--21 September 1995, pp. 1381--1384, 1995."},{"key":"e_1_3_2_2_47_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2015.7178964"},{"key":"e_1_3_2_2_48_1","volume-title":"Specaugment: A simple data augmentation method for automatic speech recognition. arXiv preprint arXiv:1904.08779","author":"Park D. S.","year":"2019","unstructured":"Park, D. S., Chan, W., Zhang, Y., Chiu, C.-C., Zoph, B., Cubuk, E. D., and Le, Q. V. Specaugment: A simple data augmentation method for automatic speech recognition. arXiv preprint arXiv:1904.08779, 2019."},{"key":"e_1_3_2_2_49_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2019.8683690"},{"key":"e_1_3_2_2_50_1","volume-title":"J., Kumar, S., and McMahan, H. B. Adaptive federated optimization. arXiv preprint arXiv:2003.00295","author":"Kumar","year":"2020","unstructured":"Kumar, and McMahan]reddi2020adaptiveReddi, S., Charles, Z., Zaheer, M., Garrett, Z., Rush, K., Konevc n?, J., Kumar, S., and McMahan, H. B. Adaptive federated optimization. arXiv preprint arXiv:2003.00295, 2020."},{"key":"e_1_3_2_2_51_1","volume-title":"On the convergence of federated optimization in heterogeneous networks. arXiv preprint arXiv:1812.06127, 3: 3","author":"Sahu A. K.","year":"2018","unstructured":"Sahu, A. K., Li, T., Sanjabi, M., Zaheer, M., Talwalkar, A., and Smith, V. On the convergence of federated optimization in heterogeneous networks. arXiv preprint arXiv:1812.06127, 3: 3, 2018."},{"key":"e_1_3_2_2_52_1","volume-title":"Measuring the effects of data parallelism on neural network training. arXiv preprint arXiv:1811.03600","author":"Shallue C. J.","year":"2018","unstructured":"Shallue, C. J., Lee, J., Antognini, J., Sohl-Dickstein, J., Frostig, R., and Dahl, G. E. Measuring the effects of data parallelism on neural network training. arXiv preprint arXiv:1811.03600, 2018."},{"key":"e_1_3_2_2_53_1","volume-title":"Don't decay the learning rate, increase the batch size","author":"Smith S. L.","year":"2018","unstructured":"Smith, S. L., Kindermans, P.-J., Ying, C., and Le, Q. V. Don't decay the learning rate, increase the batch size, 2018."},{"key":"e_1_3_2_2_54_1","doi-asserted-by":"publisher","DOI":"10.1109\/ASRU.2017.8268917"},{"key":"e_1_3_2_2_55_1","doi-asserted-by":"publisher","DOI":"10.21437\/Interspeech.2015-354"},{"key":"e_1_3_2_2_56_1","volume-title":"Optimizing network performance for distributed dnn training on gpu clusters: Imagenet\/alexnet training in 1.5 minutes","author":"Sun P.","year":"2019","unstructured":"Sun, P., Feng, W., Han, R., Yan, S., and Wen, Y. Optimizing network performance for distributed dnn training on gpu clusters: Imagenet\/alexnet training in 1.5 minutes, 2019."},{"key":"e_1_3_2_2_57_1","first-page":"2175","volume-title":"Interspeech","author":"Swarup P.","year":"2019","unstructured":"Swarup, P., Maas, R., Garimella, S., Mallidi, S. H., and Hoffmeister, B. Improving asr confidence scores for alexa using acoustic and hypothesis embeddings. In Interspeech, pp. 2175--2179, 2019."},{"key":"e_1_3_2_2_58_1","doi-asserted-by":"publisher","DOI":"10.1109\/MIS.2020.2993966"},{"key":"e_1_3_2_2_59_1","volume-title":"Federated learning with matched averaging. arXiv preprint arXiv:2002.06440","author":"Wang","year":"2020","unstructured":"Wang, Yurochkin, Sun, Papailiopoulos, and Khazaeni]wang2020federatedWang, H., Yurochkin, M., Sun, Y., Papailiopoulos, D., and Khazaeni, Y. Federated learning with matched averaging. arXiv preprint arXiv:2002.06440, 2020 a ."},{"key":"e_1_3_2_2_60_1","volume-title":"Tackling the objective inconsistency problem in heterogeneous federated optimization. arXiv preprint arXiv:2007.07481","author":"Wang","year":"2020","unstructured":"Wang, Liu, Liang, Joshi, and Poor]wang2020tacklingWang, J., Liu, Q., Liang, H., Joshi, G., and Poor, H. V. Tackling the objective inconsistency problem in heterogeneous federated optimization. arXiv preprint arXiv:2007.07481, 2020 b ."},{"key":"e_1_3_2_2_61_1","volume-title":"Semi-supervised learning with data augmentation for end-to-end asr. arXiv preprint arXiv:2007.13876","author":"Ferrer","year":"2020","unstructured":"Ferrer, and Zhan]weninger2020semiWeninger, F., Mana, F., Gemello, R., Andr\u00e9s-Ferrer, J., and Zhan, P. Semi-supervised learning with data augmentation for end-to-end asr. arXiv preprint arXiv:2007.13876, 2020."},{"key":"e_1_3_2_2_62_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00046"},{"key":"e_1_3_2_2_63_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP39728.2021.9414079"},{"key":"e_1_3_2_2_64_1","volume-title":"Scaling sgd batch size to 32k for imagenet training. arXiv preprint arXiv:1708.03888, 6: 12","author":"You Y.","year":"2017","unstructured":"You, Y., Gitman, I., and Ginsburg, B. Scaling sgd batch size to 32k for imagenet training. arXiv preprint arXiv:1708.03888, 6: 12, 2017."},{"key":"e_1_3_2_2_65_1","volume-title":"idlg: Improved deep leakage from gradients. arXiv preprint arXiv:2001.02610","author":"Zhao B.","year":"2020","unstructured":"Zhao, B., Mopuri, K. R., and Bilen, H. idlg: Improved deep leakage from gradients. arXiv preprint arXiv:2001.02610, 2020."},{"key":"e_1_3_2_2_66_1","first-page":"32","article-title":"Deep leakage from gradients","author":"Zhu L.","year":"2019","unstructured":"Zhu, L., Liu, Z., and Han, S. Deep leakage from gradients. Advances in Neural Information Processing Systems, 32, 2019.","journal-title":"Advances in Neural Information Processing Systems"}],"event":{"name":"KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Washington DC USA","acronym":"KDD '22","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3534678.3539174","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3534678.3539174","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:02:59Z","timestamp":1750186979000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3534678.3539174"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,14]]},"references-count":66,"alternative-id":["10.1145\/3534678.3539174","10.1145\/3534678"],"URL":"https:\/\/doi.org\/10.1145\/3534678.3539174","relation":{},"subject":[],"published":{"date-parts":[[2022,8,14]]},"assertion":[{"value":"2022-08-14","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}