Abstract
Federated learning is a deep learning optimization method that can solve user privacy leakage, and it has positive significance in applying industrial equipment fault diagnosis. However, edge nodes in industrial scenarios are resource-constrained, and it is challenging to meet the computational and communication resource consumption during federated training. The heterogeneity and autonomy of edge nodes will also reduce the efficiency of synchronization optimization. This paper proposes an efficient asynchronous federated learning method to solve this problem. This method allows edge nodes to select part of the model from the cloud for asynchronous updates based on local data distribution, thereby reducing the amount of calculation and communication and improving the efficiency of federated learning. Compared with the original federated learning, this method can reduce the resource requirements at the edge, reduce communication, and improve the training speed in heterogeneous edge environments. This paper uses a heterogeneous edge computing environment composed of multiple computing platforms to verify the effectiveness of the proposed method.
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References
Sisinni E, Saifullah A, Han S (2018) Industrial internet of things : challenges. Opport Direct 14(11):4724–4734
Shi W, Cao J, Zhang Q, Li Y, Xu L (2016) Edge computing: vision and challenges. IEEE Internet Things J 3(5):637–646. https://doi.org/10.1109/JIOT.2016.2579198
McMahan HB, Moore E, Ramage D, y Arcas BA (2016) Federated learning of deep networks using model averaging. arXiv:1602.05629
Brendan McMahan H, Moore E, Ramage D, Hampson S, Agüera y Arcas B (2017)Communication-efficient learning of deep networks from decentralized data. Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, AISTATS 2017
Yang Q, Liu Y, Chen T, Tong Y (2019) Federated machine learning: concept and applications. ACM Trans Intel Syst Technol 10(2):1–19. https://doi.org/10.1145/3298981
Pan X, Chen J, Monga R, Bengio S, Jozefowicz R (2017) Revisiting distributed synchronous SGD. pp 1–10 arXiv:1702.05800
Wang S, Tuor T, Salonidis T, Leung KK, Makaya C, He T, Chan K (2019) Adaptive federated learning in resource constrained edge computing systems. IEEE J Sel Areas Commun 37(6):1205–1221. https://doi.org/10.1109/JSAC.2019.2904348
Xie C, Koyejo S, Gupta I (2019) Asynchronous federated optimization. arXiv:1903.03934
Lu Y, Huang X, Dai Y, Maharjan S, Zhang Y (2020) Differentially private asynchronous federated learning for mobile edge computing in urban informatics. IEEE Trans Industr Inf 16(3):2134–2143. https://doi.org/10.1109/TII.2019.2942179
Chen Y, Sun X, Jin Y (2019) Communication-efficient federated deep learning with layerwise asynchronous model update and temporally weighted aggregation (Iid) 1–10
Deng S, Zhao H, Fang W, Yin J, Dustdar S, Zomaya AY (2020) Edge intelligence: the confluence of edge computing and artificial intelligence. IEEE Internet Things J 7(8):7457–7469. https://doi.org/10.1109/JIOT.2020.2984887
Wang X, Han Y, Wang C, Zhao Q, Chen X, Chen M (2019) In-edge ai: intelligentizing mobile edge computing, caching and communication by federated learning. IEEE Netw 33(5):156–165. https://doi.org/10.1109/MNET.2019.1800286
Smith V, Chiang CK, Sanjabi M, Talwalkar A (2017) Federated multi-task learning. Adv Neural Inform Process Syst 2017(Nips), 4425–4435. arXiv:1705.10467
McMahan JKHB, Yu FX, Richtárik P, Suresh AT, Bacon D (2017) Federated learning: Strategies for improving communication efficiency
Zhu H, Jin Y (2019) Multi-objective evolutionary federated learning. IEEE Trans Neural Netw Learn Syst. https://doi.org/10.1109/tnnls.2019.2919699
Roy D, Panda P, Roy K (2020) Tree-CNN: a hierarchical deep convolutional neural network for incremental learning. Neural Netw 121:148–160. https://doi.org/10.1016/j.neunet.2019.09.010
Teerapittayanon S, McDanel B, Kung HT (2016) BranchyNet: fast inference via early exiting from deep neural networks. Proc Int Conf Pattern Recogn. https://doi.org/10.1109/ICPR.2016.7900006
Wang Q, Wang K, Li Q, Yang Z, Jin G, Wang H (2020) MBNN: a multi-branch neural network capable of utilizing industrial sample unbalance for fast inference. IEEE Sens J 21(2):1. https://doi.org/10.1109/jsen.2020.3017686
Li X, Huang K, Yang W, Wang S, Zhang Z (2019) On the Convergence of FedAvg on Non-IID Data. arXiv:1907.02189
Bearing Data Center. https://csegroups.case.edu/bearingdatacenter/home
Zhao Y, Li M, Lai L, Suda N, Civin D, Chandra V (2018) Federated learning with non-IID data. arXiv:1806.00582
Cao P, Zhang S, Tang J (2018) Preprocessing-free gear fault diagnosis using small datasets with deep convolutional neural network-based transfer learning. IEEE Access 6:26241–26253. https://doi.org/10.1109/ACCESS.2018.2837621
Gear Fault Data. https://figshare.com/articles/Gear_Fault_Data/6127874/1
Wen L, Li X, Gao L, Zhang Y (2018) A new convolutional neural network-based data-driven fault diagnosis method. IEEE Trans Industr Electron 65(7):5990–5998. https://doi.org/10.1109/TIE.2017.2774777
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Wang, Q., Li, Q., Wang, K. et al. Efficient federated learning for fault diagnosis in industrial cloud-edge computing. Computing 103, 2319–2337 (2021). https://doi.org/10.1007/s00607-021-00970-6
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DOI: https://doi.org/10.1007/s00607-021-00970-6