[1] 
Roh Y, Heo G, Whang S E. A survey on data collection for machine learning: a big dataai integration perspective[J]. IEEE Transactions on Knowledge and Data Engineering, 2019, 33(4): 13281347.
doi: 10.1109/TKDE.2019.2946162

[2] 
Krizhevsky A, Sutskever I, Hinton G E. Imagenet classification with deep convolutional neural networks[J]. Advances in neural information processing systems, 2012, 25(2): 10971105.

[3] 
McMahan H B, Moore E, Ramage D, et al. Federated learning of deep networks using model averaging[J]. arXiv preprint arXiv:1602.05629, 2016.

[4] 
Ramanan P, Nakayama K. Baffle: Blockchain based aggregator free federated learning[C]// 2020 IEEE International Conference on Blockchain (Blockchain), IEEE, 2020: 7281.

[5] 
Kim H, Park J, Bennis M, et al. Blockchained ondevice federated learning[J]. IEEE Communications Letters, 2019, 24(6): 12791283.
doi: 10.1109/LCOMM.2019.2921755

[6] 
McMahan B, Moore E, Ramage D, et al. Communicationefficient learning of deep networks from decentralized data[C]// Artificial intelligence and statistics, PMLR, 2017: 12731282.

[7] 
Li Y, Chen C, Liu N, et al. A blockchainbased decentralized federated learning framework with committee consensus[J]. IEEE Network, 2020, 35(1): 234241.

[8] 
Awan S, Li F, Luo B, et al. Poster: A reliable and accountable privacypreserving federated learning framework using the blockchain[C]// Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security, 2019: 25612563.

[9] 
Lugan S, Desbordes P, Brion E, et al. Secure architectures implementing trusted coalitions for blockchained distributed learning (TCLearn)[J]. IEEE Access, 2019, 7: 181789181799.
doi: 10.1109/ACCESS.2019.2959220

[10] 
Zhu X, Li H, Yu Y. BlockchainBased privacy preserving deep learning[C]// International Conference on Information Security and Cryptology, Springer, Cham, 2018: 370383.

[11] 
Chen X, Ji J, Luo C, et al. When machine learning meets blockchain: A decentralized, privacypreserving and secure design[C]// 2018 IEEE international conference on big data (big data), IEEE, 2018: 11781187.

[12] 
Lu Y, Huang X, Dai Y, et al. Blockchain and federated learning for privacypreserved data sharing in industrial IoT[J]. IEEE Transactions on Industrial Informatics, 2019, 16(6): 41774186.
doi: 10.1109/TII.2019.2942190

[13] 
Liu Y, Peng J, Kang J, et al. A secure federated learning framework for 5G networks[J]. IEEE Wireless Communications, 2020, 27(4): 2431.

[14] 
Chen L, Charles Z, Papailiopoulos D. Draco: Robust distributed training via redundant gradients[J]. arXiv preprint arXiv:1803.09877, 2018.

[15] 
Guerraoui R, Rouault S. The hidden vulnerability of distributed learning in byzantium[C]// International Conference on Machine Learning, PMLR, 2018: 35213530.

[16] 
Blanchard P, El Mhamdi E M, Guerraoui R, et al. Machine learning with adversaries: Byzantine tolerant gradient descent[J]. Advances in Neural Information Processing Systems, 2017, 30.

[17] 
MuñozGonzález L, Co K T, Lupu E C. Byzantinerobust federated machine learning through adaptive model averaging[J]. arXiv preprint arXiv:1909.05125, 2019.

[18] 
Yousefpour A, Shilov I, Sablayrolles A, et al. Opacus: Userfriendly differential privacy library in PyTorch[J]. arXiv preprint arXiv:2109.12298, 2021.

[19] 
Nakamoto S. Bitcoin: A peertopeer electronic cash system[J]. Decentralized Business Review, 2008: 21260.

[20] 
工信部. 中国区块链技术和应用发展白皮书[R/OL]. [20161018]. http://www.199it.com/archives/526865.html.

[21] 
陈凯. 深度学习模型的高效训练算法研究[D]. 中国科学技术大学, 2016.

[22] 
Chen K, Huo Q. Scalable training of deep learning machines by incremental block training with intrablock parallel optimization and blockwise modelupdate filtering[C]// 2016 ieee international conference on acoustics, speech and signal processing (icassp), IEEE, 2016: 58805884.

[23] 
Dwork C. Differential privacy: A survey of results[C]// International conference on theory and applications of models of computation. Springer, Berlin, Heidelberg, 2008: 119.

[24] 
Dwork C, McSherry F, Nissim K, et al. Calibrating noise to sensitivity in private data analysis[C]// Theory of cryptography conference. Springer, Berlin, Heidelberg, 2006: 265284.

[25] 
McSherry F, Talwar K. Mechanism design via differential privacy[C]// 48th Annual IEEE Symposium on Foundations of Computer Science (FOCS'07), IEEE, 2007: 94103.

[26] 
Dwork C, Kenthapadi K, McSherry F, et al. Our data, ourselves: Privacy via distributed noise generation[C]// Annual international conference on the theory and applications of cryptographic techniques, Springer, Berlin, Heidelberg, 2006: 486503.

[27] 
Lecun Y, Cortes C. The MNIST database of handwritten digits[J/OL]. 2010. http://yann.lecun.com/exdb/mnist/.
