Abstract
With increasing concern for privacy issues in data, federated learning has emerged as one of the most prevalent approaches to collaboratively train statistical models without disclosing raw data. However, heterogeneity among clients in federated learning hinders optimization convergence and generalization performance. For example, clients usually differ in data distributions, network conditions, input/output dimensions, and model architectures, leading to the misalignment of clients’ participation in training and degrading the model performance. In this work, we propose PFedRe, a personalized approach that introduces individual relevance, measured by Wasserstein distances among dummy datasets, into client selection in federated learning. The server generates dummy datasets from the inversion of local model updates, identifies clients with large distribution divergences, and aggregates updates from high relevant clients. Theoretically, we perform a convergence analysis of PFedRe and quantify how selection affects the convergence rate. We empirically demonstrate the efficacy of our framework on a variety of non-IID datasets. The results show that PFedRe outperforms other client selection baselines in the context of heterogeneous settings.
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References
Blanchard, P., El Mhamdi, E.M., Guerraoui, R., Stainer, J.: Machine learning with adversaries: byzantine tolerant gradient descent. In: Advances in Neural Information Processing Systems, vol. 30 (2017)
Briggs, C., Fan, Z., Andras, P.: Federated learning with hierarchical clustering of local updates to improve training on non-iid data. In: 2020 International Joint Conference on Neural Networks (IJCNN), pp. 1–9. IEEE (2020)
Cao, X., Fang, M., Liu, J., Gong, N.Z.: Fltrust: byzantine-robust federated learning via trust bootstrap**. In: ISOC Network and Distributed System Security Symposium (NDSS) (2021)
Cho, Y.J., Gupta, S., Joshi, G., Yağan, O.: Bandit-based communication-efficient client selection strategies for federated learning. In: 2020 54th Asilomar Conference on Signals, Systems, and Computers, pp. 1066–1069. IEEE (2020)
Cohen, G., Afshar, S., Tapson, J., Van Schaik, A.: EMNIST: extending MNIST to handwritten letters. In: 2017 International Joint Conference on Neural Networks (IJCNN), pp. 2921–2926. IEEE (2017)
Deng, Y., Kamani, M.M., Mahdavi, M.: Adaptive personalized federated learning. ar**v preprint ar**v:2003.13461 (2020)
Fallah, A., Mokhtari, A., Ozdaglar, A.: On the convergence theory of gradient-based model-agnostic meta-learning algorithms. In: International Conference on Artificial Intelligence and Statistics, pp. 1082–1092. PMLR (2020)
Fallah, A., Mokhtari, A., Ozdaglar, A.: Personalized federated learning with theoretical guarantees: a model-agnostic meta-learning approach. Adv. Neural. Inf. Process. Syst. 33, 3557–3568 (2020)
Fang, M., Cao, X., Jia, J., Gong, N.: Local model poisoning attacks to byzantine-robust federated learning. In: 29th USENIX Security Symposium (USENIX Security 20), pp. 1605–1622 (2020)
Ghosh, A., Chung, J., Yin, D., Ramchandran, K.: An efficient framework for clustered federated learning. Adv. Neural. Inf. Process. Syst. 33, 19586–19597 (2020)
Go, A., Bhayani, R., Huang, L.: Twitter sentiment classification using distant supervision. CS224N project report, Stanford 1(12), 2009 (2009)
Goetz, J., Malik, K., Bui, D., Moon, S., Liu, H., Kumar, A.: Active federated learning. ar**v preprint ar**v:1909.12641 (2019)
Guerraoui, R., Rouault, S., et al.: The hidden vulnerability of distributed learning in byzantium. In: International Conference on Machine Learning, pp. 3521–3530. PMLR (2018)
Hanzely, F., Richtárik, P.: Federated learning of a mixture of global and local models. ar**v preprint ar**v:2002.05516 (2020)
Jiang, Y., Konečnỳ, J., Rush, K., Kannan, S.: Improving federated learning personalization via model agnostic meta learning. ar**v preprint ar**v:1909.12488 (2019)
Kairouz, P., et al.: Advances and open problems in federated learning. Found. Trends® Mach. Learn. 14(1–2), 1–210 (2021)
Karimireddy, 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)
Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images (2009)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
Li, T., Sahu, A., Zaheer, M., Sanjabi, M., Talwalkar, A., Smith, V.: Federated optimization in heterogeneous networks. Proc. Mach. Learn. Syst. 2, 429–450 (2020)
Liang, P.P., et al.: Think locally, act globally: federated learning with local and global representations. ar**v preprint ar**v:2001.01523 (2020)
Ma, Z., Lu, Y., Li, W., Yi, J., Cui, S.: PFEDATT: attention-based personalized federated learning on heterogeneous clients. In: Asian Conference on Machine Learning, pp. 1253–1268. PMLR (2021)
Mansour, Y., Mohri, M., Ro, J., Suresh, A.T.: Three approaches for personalization with applications to federated learning. ar**v preprint ar**v:2002.10619 (2020)
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)
Nagalapatti, L., Narayanam, R.: Game of gradients: mitigating irrelevant clients in federated learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 9046–9054 (2021)
Paszke, A., et al.: Pytorch: an imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems, vol. 32 (2019)
Prakash, S., Avestimehr, A.S.: Mitigating byzantine attacks in federated learning. ar**v preprint ar**v:2010.07541 (2020)
Shejwalkar, V., Houmansadr, A.: Manipulating the byzantine: optimizing model poisoning attacks and defenses for federated learning. In: NDSS (2021)
Smith, V., Chiang, C.K., Sanjabi, M., Talwalkar, A.: Federated multi-task learning. ar**v preprint ar**v:1705.10467 (2017)
T Dinh, C., Tran, N., Nguyen, T.: Personalized federated learning with MOREAU envelopes. In: Advances in Neural Information Processing Systems, vol. 33 (2020)
Tan, A.Z., Yu, H., Cui, L., Yang, Q.: Towards personalized federated learning. ar**v preprint ar**v:2103.00710 (2021)
Tang, M., Ning, X., Wang, Y., Wang, Y., Chen, Y.: FEDGP: correlation-based active client selection for heterogeneous federated learning. ar**v preprint ar**v:2103.13822 (2021)
Wang, H., Kaplan, Z., Niu, D., Li, B.: Optimizing federated learning on non-IID data with reinforcement learning. In: IEEE INFOCOM 2020-IEEE Conference on Computer Communications, pp. 1698–1707. IEEE (2020)
**e, C., Koyejo, S., Gupta, I.: Zeno: distributed stochastic gradient descent with suspicion-based fault-tolerance. In: International Conference on Machine Learning, pp. 6893–6901. PMLR (2019)
**e, C., Koyejo, S., Gupta, I.: Zeno++: Robust fully asynchronous SGD. In: International Conference on Machine Learning, pp. 10495–10503. PMLR (2020)
Yin, D., Chen, Y., Kannan, R., Bartlett, P.: Byzantine-robust distributed learning: Towards optimal statistical rates. In: International Conference on Machine Learning, pp. 5650–5659. PMLR (2018)
Acknowledgements
The work was supported in part by the Key Area R &D Program of Guangdong Province with grant No. 2018B030338001, by the National Key R &D Program of China with grant No. 2018YFB1800800, by Shenzhen Outstanding Talents Training Fund, and by Guangdong Research Project No. 2017ZT07X152 and 2021A1515011825.
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Ma, Z., Lu, Y., Li, W., Cui, S. (2023). Beyond Random Selection: A Perspective from Model Inversion in Personalized Federated Learning. In: Amini, MR., Canu, S., Fischer, A., Guns, T., Kralj Novak, P., Tsoumakas, G. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2022. Lecture Notes in Computer Science(), vol 13716. Springer, Cham. https://doi.org/10.1007/978-3-031-26412-2_35
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