{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T15:53:07Z","timestamp":1783439587553,"version":"3.54.6"},"publisher-location":"Cham","reference-count":105,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031732256","type":"print"},{"value":"9783031732263","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T00:00:00Z","timestamp":1730419200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T00:00:00Z","timestamp":1730419200000},"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\/978-3-031-73226-3_6","type":"book-chapter","created":{"date-parts":[[2024,10,31]],"date-time":"2024-10-31T15:02:57Z","timestamp":1730386977000},"page":"89-109","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["BAFFLE: A Baseline of\u00a0Backpropagation-Free Federated Learning"],"prefix":"10.1007","author":[{"given":"Haozhe","family":"Feng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianyu","family":"Pang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chao","family":"Du","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuicheng","family":"Yan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Min","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,11,1]]},"reference":[{"key":"6_CR1","doi-asserted-by":"crossref","unstructured":"Abadi, M., et al.: Deep learning with differential privacy. In: Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, Vienna, Austria, 24\u201328 October 2016 (2016)","DOI":"10.1145\/2976749.2978318"},{"key":"6_CR2","doi-asserted-by":"crossref","unstructured":"Acharya, J., Canonne, C.L., Tyagi, H.: Inference under information constraints I: lower bounds from chi-square contraction. IEEE Trans. Inf. Theory (2020)","DOI":"10.1109\/TIT.2020.3028440"},{"issue":"3\u20134","key":"6_CR3","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1016\/j.crma.2010.12.014","volume":"349","author":"R Adamczak","year":"2011","unstructured":"Adamczak, R., Litvak, A.E., Pajor, A., Tomczak-Jaegermann, N.: Sharp bounds on the rate of convergence of the empirical covariance matrix. C.R. Math. 349(3\u20134), 195\u2013200 (2011)","journal-title":"C.R. Math."},{"key":"6_CR4","unstructured":"Alistarh, D., Grubic, D., Li, J., Tomioka, R., Vojnovic, M.: QSGD: communication-efficient SGD via gradient quantization and encoding. In: Advances in Neural Information Processing Systems (NeurIPS) (2017)"},{"key":"6_CR5","unstructured":"Bagdasaryan, E., Veit, A., Hua, Y., Estrin, D., Shmatikov, V.: How to backdoor federated learning. In: International Conference on Artificial Intelligence and Statistics (AISTATS) (2020)"},{"key":"6_CR6","unstructured":"Balakrishnan, R., Li, T., Zhou, T., Himayat, N., Smith, V., Bilmes, J.: Diverse client selection for federated learning via submodular maximization. In: International Conference on Learning Representations (ICLR) (2022)"},{"key":"6_CR7","unstructured":"Barnes, L.P., Han, Y., \u00d6zg\u00fcr, A.: Lower bounds for learning distributions under communication constraints via fisher information. J. Mach. Learn. Res. (JMLR) (2020)"},{"key":"6_CR8","doi-asserted-by":"crossref","unstructured":"Basu, D., Data, D., Karakus, C., Diggavi, S.: Qsparse-Local-SGD: distributed SGD with quantization, sparsification and local computations. In: Advances in Neural Information Processing Systems (NeurIPS) (2019)","DOI":"10.1109\/JSAIT.2020.2985917"},{"key":"6_CR9","unstructured":"Bhagoji, A.N., Chakraborty, S., Mittal, P., Calo, S.: Analyzing federated learning through an adversarial lens. In: International Conference on Machine Learning (ICML) (2019)"},{"key":"6_CR10","doi-asserted-by":"crossref","unstructured":"Bonawitz, K.A., et al.: Practical secure aggregation for privacy-preserving machine learning. In: ACM Conference on Computer and Communications Security (CCS) (2017)","DOI":"10.1145\/3133956.3133982"},{"key":"6_CR11","unstructured":"Bonawitz, K., et al.: Practical secure aggregation for federated learning on user-held data. arXiv preprint arXiv:1611.04482 (2016)"},{"key":"6_CR12","doi-asserted-by":"crossref","unstructured":"Bonawitz, K., et al.: Practical secure aggregation for privacy-preserving machine learning. In: ACM SIGSAC Conference on Computer and Communications Security (2017)","DOI":"10.1145\/3133956.3133982"},{"key":"6_CR13","unstructured":"Borgnia, E., et al.: DP-InstaHide: provably defusing poisoning and backdoor attacks with differentially private data augmentations. arXiv preprint arXiv:2103.02079 (2021)"},{"key":"6_CR14","unstructured":"Bradbury, J., et al.: JAX: composable transformations of Python+NumPy programs (2018). http:\/\/github.com\/google\/jax"},{"key":"6_CR15","doi-asserted-by":"crossref","unstructured":"Braverman, M., Garg, A., Ma, T., Nguyen, H.L., Woodruff, D.P.: Communication lower bounds for statistical estimation problems via a distributed data processing inequality. In: ACM Symposium on Theory of Computing (2016)","DOI":"10.1145\/2897518.2897582"},{"key":"6_CR16","unstructured":"Caldas, S., Kone\u010dny, J., McMahan, H.B., Talwalkar, A.: Expanding the reach of federated learning by reducing client resource requirements. arXiv preprint arXiv:1812.07210 (2018)"},{"key":"6_CR17","unstructured":"Caldas, S., et al.: LEAF: a benchmark for federated settings. CoRR (2018)"},{"issue":"17","key":"6_CR18","doi-asserted-by":"publisher","first-page":"e2024789118","DOI":"10.1073\/pnas.2024789118","volume":"118","author":"M Chen","year":"2021","unstructured":"Chen, M., Shlezinger, N., Poor, H.V., Eldar, Y.C., Cui, S.: Communication-efficient federated learning. Proc. Natl. Acad. Sci. 118(17), e2024789118 (2021)","journal-title":"Proc. Natl. Acad. Sci."},{"key":"6_CR19","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1016\/j.ins.2020.02.037","volume":"522","author":"Y Chen","year":"2020","unstructured":"Chen, Y., Luo, F., Li, T., Xiang, T., Liu, Z., Li, J.: A training-integrity privacy-preserving federated learning scheme with trusted execution environment. Inf. Sci. 522, 69\u201379 (2020)","journal-title":"Inf. Sci."},{"key":"6_CR20","doi-asserted-by":"crossref","unstructured":"Dong, Y., et al.: Black-box detection of backdoor attacks with limited information and data. In: IEEE International Conference on Computer Vision (ICCV) (2021)","DOI":"10.1109\/ICCV48922.2021.01617"},{"key":"6_CR21","unstructured":"Eichner, H., Koren, T., McMahan, B., Srebro, N., Talwar, K.: Semi-cyclic stochastic gradient descent. In: International Conference on Machine Learning (ICML) (2019)"},{"key":"6_CR22","doi-asserted-by":"crossref","unstructured":"Fang, W., Yu, Z., Jiang, Y., Shi, Y., Jones, C.N., Zhou, Y.: Communication-efficient stochastic zeroth-order optimization for federated learning. IEEE Trans. Signal Process. (2022)","DOI":"10.1109\/TSP.2022.3214122"},{"key":"6_CR23","unstructured":"Flaxman, A.D., Kalai, A.T., McMahan, H.B.: Online convex optimization in the bandit setting: gradient descent without a gradient. arXiv preprint cs\/0408007 (2004)"},{"key":"6_CR24","doi-asserted-by":"crossref","unstructured":"Gao, Y., et al.: Estimating GPU memory consumption of deep learning models. In: ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (2020)","DOI":"10.1145\/3368089.3417050"},{"key":"6_CR25","unstructured":"Geiping, J., Bauermeister, H., Dr\u00f6ge, H., Moeller, M.: Inverting gradients-how easy is it to break privacy in federated learning? In: Advances in Neural Information Processing Systems (NeurIPS) (2020)"},{"key":"6_CR26","doi-asserted-by":"crossref","unstructured":"Ghazi, B., Kumar, R., Manurangsi, P., Pagh, R.: Private counting from anonymous messages: near-optimal accuracy with vanishing communication overhead. In: International Conference on Machine Learning (ICML) (2020)","DOI":"10.1007\/978-3-030-45724-2_27"},{"key":"6_CR27","unstructured":"Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016)"},{"key":"6_CR28","unstructured":"Grill, J., et al.: Bootstrap your own latent - a new approach to self-supervised learning. In: NeurIPS 2020 (2020)"},{"key":"6_CR29","unstructured":"Hamer, J., Mohri, M., Suresh, A.T.: FedBoost: a communication-efficient algorithm for federated learning. In: International Conference on Machine Learning (ICML) (2020)"},{"key":"6_CR30","unstructured":"Han, Y., \u00d6zg\u00fcr, A., Weissman, T.: Geometric lower bounds for distributed parameter estimation under communication constraints. In: Conference on Learning Theory (COLT) (2018)"},{"key":"6_CR31","doi-asserted-by":"crossref","unstructured":"Hao, M., Li, H., Luo, X., Xu, G., Yang, H., Liu, S.: Efficient and privacy-enhanced federated learning for industrial artificial intelligence. IEEE Trans. Ind. Inform. (2019)","DOI":"10.1109\/TII.2019.2945367"},{"key":"6_CR32","unstructured":"Hard, A., et al.: Federated learning for mobile keyboard prediction. arXiv preprint arXiv:1811.03604 (2018)"},{"key":"6_CR33","unstructured":"He, C., Annavaram, M., Avestimehr, S.: Group knowledge transfer: federated learning of large CNNs at the edge. In: Advances in Neural Information Processing Systems (NeurIPS) (2020)"},{"key":"6_CR34","unstructured":"He, D., et al.: Learning physics-informed neural networks without stacked back-propagation. arXiv preprint arXiv:2202.09340 (2022)"},{"key":"6_CR35","unstructured":"Hinton, G., Srivastava, N.: CSC321: introduction to neural networks and machine learning. Lecture 10 (2010)"},{"key":"6_CR36","unstructured":"Horv\u00e1th, S., Ho, C.Y., Horvath, L., Sahu, A.N., Canini, M., Richt\u00e1rik, P.: Natural compression for distributed deep learning. arXiv preprint arXiv:1905.10988 (2019)"},{"key":"6_CR37","doi-asserted-by":"crossref","unstructured":"Howard, A., et al.: Searching for MobileNetV3. In: IEEE International Conference on Computer Vision (ICCV) (2019)","DOI":"10.1109\/ICCV.2019.00140"},{"key":"6_CR38","unstructured":"Howard, A.G., et al.: MobileNets: efficient convolutional neural networks for mobile vision applications. CoRR (2017)"},{"key":"6_CR39","unstructured":"Huang, Y., Gupta, S., Song, Z., Li, K., Arora, S.: Evaluating gradient inversion attacks and defenses in federated learning. In: Advances in Neural Information Processing Systems (NeurIPS) (2021)"},{"key":"6_CR40","unstructured":"Huh, M., Agrawal, P., Efros, A.A.: What makes ImageNet good for transfer learning? CoRR (2016)"},{"key":"6_CR41","doi-asserted-by":"crossref","unstructured":"Jia, Y., et al.: Caffe: convolutional architecture for fast feature embedding. In: Hua, K.A., Rui, Y., Steinmetz, R., Hanjalic, A., Natsev, A., Zhu, W. (eds.) Proceedings of the ACM International Conference on Multimedia (2014)","DOI":"10.1145\/2647868.2654889"},{"key":"6_CR42","unstructured":"Kairouz, P., et\u00a0al.: Advances and open problems in federated learning. Found. Trends\u00ae Mach. Learn. 14(1\u20132), 1\u2013210 (2021)"},{"key":"6_CR43","doi-asserted-by":"crossref","unstructured":"Kang, J., Xiong, Z., Niyato, D., Zou, Y., Zhang, Y., Guizani, M.: Reliable federated learning for mobile networks. IEEE Wirel. Commun. (2020)","DOI":"10.1109\/MWC.001.1900119"},{"key":"6_CR44","doi-asserted-by":"crossref","unstructured":"Kim, K., et al.: Vessels: efficient and scalable deep learning prediction on trusted processors. In: ACM Symposium on Cloud Computing (2020)","DOI":"10.1145\/3419111.3421282"},{"key":"6_CR45","doi-asserted-by":"crossref","unstructured":"Kim, Y., Sun, J., Yu, H., Jiang, X.: Federated tensor factorization for computational phenotyping. In: ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD) (2017)","DOI":"10.1145\/3097983.3098118"},{"key":"6_CR46","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR 2015 (2015)"},{"key":"6_CR47","unstructured":"Kone\u010dn\u1ef3, J., McMahan, H.B., Yu, F.X., Richt\u00e1rik, P., Suresh, A.T., Bacon, D.: Federated learning: strategies for improving communication efficiency. arXiv preprint arXiv:1610.05492 (2016)"},{"key":"6_CR48","unstructured":"Krizhevsky, A., Hinton, G.: Learning multiple layers of features from tiny images (2009)"},{"issue":"11","key":"6_CR49","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278\u20132324 (1998)","journal-title":"Proc. IEEE"},{"key":"6_CR50","unstructured":"Li, J., Luo, X., Qiao, M.: On generalization error bounds of noisy gradient methods for non-convex learning. In: International Conference on Learning Representations (ICLR) (2020)"},{"key":"6_CR51","doi-asserted-by":"publisher","first-page":"106854","DOI":"10.1016\/j.cie.2020.106854","volume":"149","author":"L Li","year":"2020","unstructured":"Li, L., Fan, Y., Tse, M., Lin, K.Y.: A review of applications in federated learning. Comput. Ind. Eng. 149, 106854 (2020)","journal-title":"Comput. Ind. Eng."},{"key":"6_CR52","unstructured":"Li, T., Hu, S., Beirami, A., Smith, V.: Ditto: fair and robust federated learning through personalization. In: International Conference on Machine Learning (ICML) (2021)"},{"key":"6_CR53","unstructured":"Li, X., Huang, K., Yang, W., Wang, S., Zhang, Z.: On the convergence of FedAvg on Non-IID data. In: International Conference on Learning Representations (ICLR) (2020)"},{"key":"6_CR54","doi-asserted-by":"crossref","unstructured":"Li, Z., Chen, L.: Communication-efficient decentralized zeroth-order method on heterogeneous data. In: WCSP 2021 (2021)","DOI":"10.1109\/WCSP52459.2021.9613620"},{"key":"6_CR55","doi-asserted-by":"crossref","unstructured":"Liu, S., Chen, P.Y., Kailkhura, B., Zhang, G., Hero III, A.O., Varshney, P.K.: A primer on zeroth-order optimization in signal processing and machine learning: Principals, recent advances, and applications. IEEE Signal Process. Mag. 37(5), 43\u201354 (2020)","DOI":"10.1109\/MSP.2020.3003837"},{"key":"6_CR56","unstructured":"Liu, Y., Suresh, A.T., Yu, F.X.X., Kumar, S., Riley, M.: Learning discrete distributions: user vs item-level privacy. In: Advances in Neural Information Processing Systems (NeurIPS) (2020)"},{"key":"6_CR57","doi-asserted-by":"crossref","unstructured":"Luo, X., Wu, Y., Xiao, X., Ooi, B.C.: Feature inference attack on model predictions in vertical federated learning. In: IEEE International Conference on Data Engineering (ICDE) (2021)","DOI":"10.1109\/ICDE51399.2021.00023"},{"key":"6_CR58","unstructured":"Lyu, L., et al.: Privacy and robustness in federated learning: attacks and defenses. IEEE Trans. Neural Netw. Learn. Syst., 1\u201321 (2022)"},{"key":"6_CR59","doi-asserted-by":"crossref","unstructured":"Ma, J., Zhang, Q., Lou, J., Ho, J.C., Xiong, L., Jiang, X.: Privacy-preserving tensor factorization for collaborative health data analysis. In: ACM International Conference on Information and Knowledge Management (CIKM) (2019)","DOI":"10.1145\/3357384.3357878"},{"key":"6_CR60","unstructured":"Marfoq, O., Neglia, G., Bellet, A., Kameni, L., Vidal, R.: Federated multi-task learning under a mixture of distributions. In: Advances in Neural Information Processing Systems (NeurIPS) (2021)"},{"key":"6_CR61","doi-asserted-by":"crossref","unstructured":"McKeen, F., et al.: Intel\u00ae software guard extensions (intel\u00ae SGX) support for dynamic memory management inside an enclave. In: Proceedings of the Hardware and Architectural Support for Security and Privacy (2016)","DOI":"10.1145\/2948618.2954331"},{"key":"6_CR62","unstructured":"McMahan, B., Moore, E., Ramage, D., Hampson, S., y\u00a0Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: International Conference on Artificial Intelligence and Statistics (AISTATS) (2017)"},{"key":"6_CR63","unstructured":"McMahan, B., Ramage, D., Talwar, K., Zhang, L.: Learning differentially private recurrent language models. In: International Conference on Learning Representations (ICLR) (2018)"},{"key":"6_CR64","doi-asserted-by":"crossref","unstructured":"Mo, F., Haddadi, H., Katevas, K., Marin, E., Perino, D., Kourtellis, N.: PPFL: privacy-preserving federated learning with trusted execution environments. In: Annual International Conference on Mobile Systems, Applications, and Services (2021)","DOI":"10.1145\/3458864.3466628"},{"key":"6_CR65","doi-asserted-by":"crossref","unstructured":"Mondal, A., More, Y., Rooparaghunath, R.H., Gupta, D.: FLATEE: federated learning across trusted execution environments. arXiv preprint arXiv:2111.06867 (2021)","DOI":"10.1109\/EuroSP51992.2021.00054"},{"key":"6_CR66","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1016\/j.ins.2019.04.012","volume":"492","author":"E Moulay","year":"2019","unstructured":"Moulay, E., L\u00e9chapp\u00e9, V., Plestan, F.: Properties of the sign gradient descent algorithms. Inf. Sci. 492, 29\u201339 (2019)","journal-title":"Inf. Sci."},{"key":"6_CR67","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3397461","volume":"45","author":"S Nakandala","year":"2020","unstructured":"Nakandala, S., Nagrecha, K., Kumar, A., Papakonstantinou, Y.: Incremental and approximate computations for accelerating deep CNN inference. ACM Trans. Database Syst. 45, 1\u201342 (2020)","journal-title":"ACM Trans. Database Syst."},{"key":"6_CR68","doi-asserted-by":"crossref","unstructured":"Nasr, M., Shokri, R., Houmansadr, A.: Comprehensive privacy analysis of deep learning: passive and active white-box inference attacks against centralized and federated learning. In: IEEE Symposium on Security and Privacy (S &P). IEEE (2019)","DOI":"10.1109\/SP.2019.00065"},{"key":"6_CR69","doi-asserted-by":"publisher","first-page":"527","DOI":"10.1007\/s10208-015-9296-2","volume":"17","author":"Y Nesterov","year":"2017","unstructured":"Nesterov, Y., Spokoiny, V.: Random gradient-free minimization of convex functions. Found. Comput. Math. 17, 527\u2013566 (2017)","journal-title":"Found. Comput. Math."},{"key":"6_CR70","unstructured":"Pang, T., Xu, K., Li, C., Song, Y., Ermon, S., Zhu, J.: Efficient learning of generative models via finite-difference score matching. In: Advances in Neural Information Processing Systems (NeurIPS) (2020)"},{"key":"6_CR71","unstructured":"Pang, T., Yang, X., Dong, Y., Su, H., Zhu, J.: Accumulative poisoning attacks on real-time data. In: Advances in Neural Information Processing Systems (NeurIPS) (2021)"},{"key":"6_CR72","unstructured":"Paszke, A., et al.: PyTorch: an imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems, vol. 32 (2019)"},{"key":"6_CR73","unstructured":"Paszke, A., et\u00a0al.: PyTorch: an imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems (NeurIPS) (2019)"},{"key":"6_CR74","unstructured":"Rothchild, D., et al.: FetchSGD: communication-efficient federated learning with sketching. In: International Conference on Machine Learning (ICML) (2020)"},{"key":"6_CR75","doi-asserted-by":"crossref","unstructured":"Sabt, M., Achemlal, M., Bouabdallah, A.: Trusted execution environment: what it is, and what it is not. In: IEEE Trustcom\/BigDataSE\/ISPA (2015)","DOI":"10.1109\/Trustcom.2015.357"},{"key":"6_CR76","doi-asserted-by":"crossref","unstructured":"Sattler, F., Wiedemann, S., M\u00fcller, K.R., Samek, W.: Robust and communication-efficient federated learning from Non-IID data. IEEE Trans. Neural Network Learn. Syst. (TNNLS) (2019)","DOI":"10.1109\/TNNLS.2019.2944481"},{"key":"6_CR77","unstructured":"Saxe, A.M., McClelland, J.L., Ganguli, S.: Exact solutions to the nonlinear dynamics of learning in deep linear neural networks. arXiv preprint arXiv:1312.6120 (2013)"},{"key":"6_CR78","doi-asserted-by":"crossref","unstructured":"Seetharaman, S., Malaviya, S., KV, R., Shukla, M., Lodha, S.: Influence based defense against data poisoning attacks in online learning. arXiv preprint arXiv:2104.13230 (2021)","DOI":"10.1109\/COMSNETS53615.2022.9668557"},{"key":"6_CR79","doi-asserted-by":"crossref","unstructured":"Sharma, H., et al.: Bit fusion: bit-level dynamically composable architecture for accelerating deep neural network. In: IEEE Annual International Symposium on Computer Architecture (ISCA) (2018)","DOI":"10.1109\/ISCA.2018.00069"},{"key":"6_CR80","doi-asserted-by":"crossref","unstructured":"Shokri, R., Stronati, M., Song, C., Shmatikov, V.: Membership inference attacks against machine learning models. In: IEEE Symposium on Security and Privacy (S &P). IEEE (2017)","DOI":"10.1109\/SP.2017.41"},{"key":"6_CR81","unstructured":"Smith, V., Chiang, C.K., Sanjabi, M., Talwalkar, A.S.: Federated multi-task learning. In: Advances in Neural Information Processing Systems (NeurIPS) (2017)"},{"key":"6_CR82","doi-asserted-by":"publisher","first-page":"1135","DOI":"10.1214\/aos\/1176345632","volume":"9","author":"CM Stein","year":"1981","unstructured":"Stein, C.M.: Estimation of the mean of a multivariate normal distribution. Ann. Stat. 9, 1135\u20131151 (1981)","journal-title":"Ann. Stat."},{"key":"6_CR83","unstructured":"Sun, Z., Kairouz, P., Suresh, A.T., McMahan, H.B.: Can you really backdoor federated learning? arXiv preprint arXiv:1911.07963 (2019)"},{"key":"6_CR84","unstructured":"Suresh, A.T., Felix, X.Y., Kumar, S., McMahan, H.B.: Distributed mean estimation with limited communication. In: International Conference on Machine Learning (ICML) (2017)"},{"key":"6_CR85","unstructured":"Tramer, F., Boneh, D.: Slalom: fast, verifiable and private execution of neural networks in trusted hardware. In: International Conference on Learning Representations (ICLR) (2019)"},{"key":"6_CR86","doi-asserted-by":"crossref","unstructured":"Truex, S., et al.: A hybrid approach to privacy-preserving federated learning. In: ACM Workshop on Artificial Intelligence and Security (2019)","DOI":"10.1145\/3338501.3357370"},{"key":"6_CR87","doi-asserted-by":"crossref","unstructured":"Truong, J.B., Gallagher, W., Guo, T., Walls, R.J.: Memory-efficient deep learning inference in trusted execution environments. In: IEEE International Conference on Cloud Engineering (IC2E) (2021)","DOI":"10.1109\/IC2E52221.2021.00031"},{"key":"6_CR88","doi-asserted-by":"crossref","unstructured":"Umuroglu, Y., Rasnayake, L., Sj\u00e4lander, M.: BISMO: a scalable bit-serial matrix multiplication overlay for reconfigurable computing. In: International Conference on Field Programmable Logic and Applications (FPL) (2018)","DOI":"10.1109\/FPL.2018.00059"},{"key":"6_CR89","doi-asserted-by":"crossref","unstructured":"Venkateswara, H., Eusebio, J., Chakraborty, S., Panchanathan, S.: Deep hashing network for unsupervised domain adaptation. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.572"},{"key":"6_CR90","unstructured":"Wang, H., et al.: Attack of the tails: Yes, you really can backdoor federated learning. In: Advances in Neural Information Processing Systems (NeurIPS) (2020)"},{"key":"6_CR91","unstructured":"Wang, H.P., Stich, S., He, Y., Fritz, M.: ProgFed: effective, communication, and computation efficient federated learning by progressive training. In: International Conference on Machine Learning (ICML) (2022)"},{"key":"6_CR92","unstructured":"Wang, J., Liu, Q., Liang, H., Joshi, G., Poor, H.V.: Tackling the objective inconsistency problem in heterogeneous federated optimization. In: Advances in Neural Information Processing Systems (NeurIPS) (2020)"},{"key":"6_CR93","doi-asserted-by":"crossref","unstructured":"Wang, K., Liu, Z., Lin, Y., Lin, J., Han, S.: HAQ: hardware-aware automated quantization with mixed precision. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)","DOI":"10.1109\/CVPR.2019.00881"},{"key":"6_CR94","doi-asserted-by":"crossref","unstructured":"Wang, L., Wang, W., Li, B.: CMFL: mitigating communication overhead for federated learning. In: IEEE International Conference on Distributed Computing Systems (ICDCS) (2019)","DOI":"10.1109\/ICDCS.2019.00099"},{"key":"6_CR95","doi-asserted-by":"publisher","first-page":"3454","DOI":"10.1109\/TIFS.2020.2988575","volume":"15","author":"K Wei","year":"2020","unstructured":"Wei, K., et al.: Federated learning with differential privacy: algorithms and performance analysis. IEEE Trans. Inf. Forensics Secur. 15, 3454\u20133469 (2020)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"issue":"3","key":"6_CR96","doi-asserted-by":"publisher","first-page":"742","DOI":"10.1007\/s11263-019-01198-w","volume":"128","author":"Y Wu","year":"2020","unstructured":"Wu, Y., He, K.: Group normalization. Int. J. Comput. Vis. 128(3), 742\u2013755 (2020)","journal-title":"Int. J. Comput. Vis."},{"key":"6_CR97","unstructured":"Xie, C., Huang, K., Chen, P.Y., Li, B.: DBA: distributed backdoor attacks against federated learning. In: International Conference on Learning Representations (ICLR) (2020)"},{"key":"6_CR98","unstructured":"Yang, T., et al.: Applied federated learning: improving google keyboard query suggestions. arXiv preprint arXiv:1812.02903 (2018)"},{"key":"6_CR99","doi-asserted-by":"crossref","unstructured":"Yang, X., et al.: An accuracy-lossless perturbation method for defending privacy attacks in federated learning. In: ACM Web Conference (WWW) (2022)","DOI":"10.1145\/3485447.3512233"},{"key":"6_CR100","doi-asserted-by":"crossref","unstructured":"Zagoruyko, S., Komodakis, N.: Wide residual networks. In: Proceedings of the British Machine Vision Conference (2016)","DOI":"10.5244\/C.30.87"},{"key":"6_CR101","unstructured":"Zeng, D., Liang, S., Hu, X., Wang, H., Xu, Z.: FedLab: a flexible federated learning framework. arXiv preprint arXiv:2107.11621 (2021)"},{"key":"6_CR102","unstructured":"Zhang, Y., Duchi, J., Jordan, M.I., Wainwright, M.J.: Information-theoretic lower bounds for distributed statistical estimation with communication constraints. In: Advances in Neural Information Processing Systems (NeurIPS) (2013)"},{"key":"6_CR103","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Jia, R., Pei, H., Wang, W., Li, B., Song, D.: The secret revealer: generative model-inversion attacks against deep neural networks. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2020)","DOI":"10.1109\/CVPR42600.2020.00033"},{"key":"6_CR104","unstructured":"Zhao, Y., Li, M., Lai, L., Suda, N., Civin, D., Chandra, V.: Federated learning with Non-IID data. arXiv preprint arXiv:1806.00582 (2018)"},{"key":"6_CR105","unstructured":"Zhu, Z., Wu, J., Yu, B., Wu, L., Ma, J.: The anisotropic noise in stochastic gradient descent: its behavior of escaping from sharp minima and regularization effects. In: International Conference on Machine Learning (ICML) (2019)"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-73226-3_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,31]],"date-time":"2024-10-31T15:14:00Z","timestamp":1730387640000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-73226-3_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,1]]},"ISBN":["9783031732256","9783031732263"],"references-count":105,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-73226-3_6","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,1]]},"assertion":[{"value":"1 November 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Milan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","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":"29 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2024.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}