Abstract
Statistical heterogeneity, especially feature distribution skewness, among the distributed data is a common phenomenon in practice, which is a challenging problem in federated learning that can lead to a degradation in the performance of the aggregated global model. In this paper, we introduce pFedV, a novel approach that leverages a variational inference perspective by incorporating a variational distribution into neural networks. During training, we add the KL-divergence term to the loss function to constrain the output distribution of layers for feature extraction and personalize the final layer of models. The experimental results demonstrate the effectiveness of our approaches in mitigating the distribution shift in feature space in federated learning.
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Notes
- 1.
The variational distribution is the output of the encoder parameterized by \(\theta \), which is equivalent to \(\theta _g\) in the previous section.
- 2.
We omitted x in the formula since all distributions are given the condition of \({\textbf {x}}\), e.g., \(q_\theta ({\textbf {z}}) = q_\theta ({\textbf {z}} \vert {\textbf {x}})\).
References
Banabilah, S., Aloqaily, M., Alsayed, E., Malik, N., Jararweh, Y.: Federated learning review: fundamentals, enabling technologies, and future applications. Inf. Process. Manage. 59(6), 103061 (2022)
Blei, D.M., Kucukelbir, A., McAuliffe, J.D.: Variational inference: a review for statisticians. J. Am. Stat. Assoc. 112(518), 859–877 (2017)
Blundell, C., Cornebise, J., Kavukcuoglu, K., Wierstra, D.: Weight uncertainty in neural network. In: International Conference on Machine Learning, pp. 1613–1622. PMLR (2015)
Chen, H.Y., Chao, W.L.: FedBE: Making Bayesian model ensemble applicable to federated learning. arXiv preprint arXiv:2009.01974 (2020)
Geng, J., et al.: Towards general deep leakage in federated learning. arXiv preprint arXiv:2110.09074 (2021)
Kairouz, P., et al.: Advances and open problems in federated learning. Found. Trends® Mach. Learn. 14(1–2), 1–210 (2021)
Karimireddy, S.P., Kale, S., Mohri, M., Reddi, S., Stich, S., Suresh, A.T.: Scaffold: Stochastic controlled averaging for federated learning. In: International Conference on Machine Learning, pp. 5132–5143. PMLR (2020)
Khan, L.U., Saad, W., Han, Z., Hossain, E., Hong, C.S.: Federated learning for internet of things: recent advances, taxonomy, and open challenges. IEEE Commun. Surv. Tutorials PP, 1 (2021)
Kingma, D.P., Welling, M.: Auto-encoding variational Bayes. arXiv preprint arXiv:1312.6114 (2013)
Li, Q., Diao, Y., Chen, Q., He, B.: Federated learning on non-IID data silos: an experimental study. In: 2022 IEEE 38th International Conference on Data Engineering (ICDE), pp. 965–978. IEEE (2022)
Li, T., Sahu, A.K., Talwalkar, A., Smith, V.: Federated learning: Challenges, methods, and future directions. IEEE Signal Process. Mag. 37(3), 50–60 (2020)
Li, T., Sahu, A.K., Zaheer, M., Sanjabi, M., Talwalkar, A., Smith, V.: Federated optimization in heterogeneous networks. Proceed. Mach. Learn. Syst. 2, 429–450 (2020)
Li, X., Huang, K., Yang, W., Wang, S., Zhang, Z.: On the convergence of FedAvg on non-IID data. arXiv preprint arXiv:1907.02189 (2019)
Li, X., Jiang, M., Zhang, X., Kamp, M., Dou, Q.: FedBN: Federated learning on non-IID features via local batch normalization. arXiv preprint arXiv:2102.07623 (2021)
Liu, L., Zheng, F., Chen, H., Qi, G.J., Huang, H., Shao, L.: A bayesian federated learning framework with online Laplace approximation. arXiv preprint arXiv:2102.01936 (2021)
McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial intelligence and statistics, pp. 1273–1282. PMLR (2017)
Mou, Y., Geng, J., Welten, S., Rong, C., Decker, S., Beyan, O.: Optimized federated learning on class-biased distributed data sources. In: Machine Learning and Principles and Practice of Knowledge Discovery in Databases. ECML PKDD 2021. Communications in Computer and Information Science, vol. 1524, pp. 146–158. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-93736-2_13
Nguyen, D.C., et al.: Federated learning for smart healthcare: a survey. ACM Comput. Surv. (CSUR) 55(3), 1–37 (2022)
Odaibo, S.: Tutorial: Deriving the standard variational autoencoder (VAE) loss function. arXiv preprint arXiv:1907.08956 (2019)
Sahu, A.K., Li, T., Sanjabi, M., Zaheer, M., Talwalkar, A., Smith, V.: On the convergence of federated optimization in heterogeneous networks. arXiv preprint arXiv:1812.06127 3, 3 (2018)
Wang, J., Liu, Q., Liang, H., Joshi, G., Poor, H.V.: Tackling the objective inconsistency problem in heterogeneous federated optimization. Adv. Neural. Inf. Process. Syst. 33, 7611–7623 (2020)
Yang, Q., Liu, Y., Chen, T., Tong, Y.: Federated machine learning: concept and applications. ACM Trans. Intell. Syst. Technol. (TIST) 10(2), 1–19 (2019)
Yang, W., Zhang, Y., Ye, K., Li, L., Xu, C.-Z.: FFD: a federated learning based method for credit card fraud detection. In: Chen, K., Seshadri, S., Zhang, L.-J. (eds.) BIGDATA 2019. LNCS, vol. 11514, pp. 18–32. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-23551-2_2
Zhang, X., Li, Y., Li, W., Guo, K., Shao, Y.: Personalized federated learning via variational Bayesian inference. In: International Conference on Machine Learning, pp. 26293–26310. PMLR (2022)
Acknowledgements
This work was supported by the German Ministry for Research and Education (BMBF) projects CORD_MI, POLAR_MI, Leuko-Expert and WestAI (Grant no. No. 01ZZ1911M, 01ZZ1910E, ZMVI1-2520DAT94C and 01IS22094D, respectively), CLARIFY Project (Marie Skłodowska-Curie under Grant no. 860627), and by National Natural Science Foundation of China (NSFC) Project (No. 62106121). This research was supported by Public Computing Cloud, Renmin University of China.
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Mou, Y., Geng, J., Zhou, F., Beyan, O., Rong, C., Decker, S. (2023). pFedV: Mitigating Feature Distribution Skewness via Personalized Federated Learning with Variational Distribution Constraints. In: Kashima, H., Ide, T., Peng, WC. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2023. Lecture Notes in Computer Science(), vol 13936. Springer, Cham. https://doi.org/10.1007/978-3-031-33377-4_22
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