Abstract
Federated learning enables collaborative knowledge acquisition among clinical institutions while preserving data privacy. However, feature heterogeneity across institutions can compromise the global model’s performance and generalization capability. Existing methods often adjust aggregation weights dynamically to improve the global model’s generalization but rely heavily on the local models’ performance or reliability, excluding an explicit measure of the generalization gap arising from deploying the global model across varied local datasets. To address this issue, we propose FedEvi, a method that adjusts the aggregation weights based on the generalization gap between the global model and each local dataset and the reliability of local models. We utilize a Dirichlet-based evidential model to disentangle the uncertainty representation of each local model and the global model into epistemic uncertainty and aleatoric uncertainty. Then, we quantify the global generalization gap using the epistemic uncertainty of the global model and assess the reliability of each local model using its aleatoric uncertainty. Afterward, we design aggregation weights using the global generalization gap and local reliability. Comprehensive experimentation reveals that FedEvi consistently surpasses 12 state-of-the-art methods across three real-world multi-center medical image segmentation tasks, demonstrating the effectiveness of FedEvi in bolstering the generalization capacity of the global model in heterogeneous federated scenarios. The code will be available at https://github.com/JiayiChen815/FedEvi.
J. Chen and B. Ma—Contributed equally.
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References
Almazroa, A., Alodhayb, S., Osman, E., et al.: Retinal fundus images for glaucoma analysis: the RIGA dataset. In: Medical Imaging 2018: Imaging Informatics for Healthcare, Research, and Applications, vol. 10579, pp. 55–62 (2018)
Bernal, J., Sánchez, F.J., Fernández-Esparrach, G., et al.: WM-DOVA maps for accurate polyp highlighting in colonoscopy: validation vs. saliency maps from physicians. Comput. Med. Imag. Grap. 43, 99–111 (2015)
Bloch, N., Madabhushi, A., Huisman, H., et al.: NCI-ISBI 2013 challenge: automated segmentation of prostate structures. TCIA 370(6), 5 (2015)
Chen, J., Ma, B., Cui, H., Xia, Y.: Think twice before selection: federated evidential active learning for medical image analysis with domain shifts. In: CVPR (2024)
Deng, Z., Li, D., Tan, S., et al.: FedGrav: an adaptive federated aggregation algorithm for multi-institutional medical image segmentation. In: Greenspan, H., et al. (eds.) MICCAI 2023. LNCS, vol. 14221, pp. 170–180. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-43895-0_16
Depeweg, S., Hernández-Lobato, J.M., Doshi-Velez, F., Udluft, S.: Decomposition of uncertainty for active learning and reliable reinforcement learning in stochastic systems. Stat 1050, 11 (2017)
Fumero, F., Alayón, S., Sanchez, J.L., et al.: Rim-one: an open retinal image database for optic nerve evaluation. In: CBMS (2011)
Guo, Y., Tang, X., Lin, T.: FedBR: improving federated learning on heterogeneous data via local learning bias reduction. In: ICML (2023)
Jha, D., Smedsrud, P.H., Riegler, M.A., et al.: Kvasir-SEG: a segmented polyp dataset. In: MMM (2020)
Jiang, M., Roth, H.R., Li, W., et al.: Fair federated medical image segmentation via client contribution estimation. In: CVPR (2023)
Kullback, S., Leibler, R.A.: On information and sufficiency. Ann. Math. Stat. 22(1), 79–86 (1951)
Lemaître, G., Martí, R., Freixenet, J., et al.: Computer-aided detection and diagnosis for prostate cancer based on mono and multi-parametric MRI: a review. Comput. Biol. Med. 60, 8–31 (2015)
Li, H., Nan, Y., Del Ser, J., Yang, G.: Region-based evidential deep learning to quantify uncertainty and improve robustness of brain tumor segmentation. Neural Comput. Appl. 1–15 (2022)
Li, T., Sahu, A.K., et al.: Federated optimization in heterogeneous networks. MLSys 2, 429–450 (2020)
Li, X., Jiang, M., et al.: FedBN: federated learning on non-IID features via local batch normalization. In: ICLR (2021)
Li, Z., Lin, T., Shang, X., Wu, C.: Revisiting weighted aggregation in federated learning with neural networks. arXiv preprint arXiv:2302.10911 (2023)
Litjens, G., Toth, R., Van De Ven, W., et al.: Evaluation of prostate segmentation algorithms for MRI: the promise12 challenge. Med. Image Anal. 18(2), 359–373 (2014)
Liu, Q., Chen, C., Qin, J., et al.: FedDG: federated domain generalization on medical image segmentation via episodic learning in continuous frequency space. In: CVPR (2021)
Ma, B., Feng, Y., Chen, G., et al.: Federated adaptive reweighting for medical image classification. Pattern Recogn. 144, 109880 (2023)
Ma, B., Zhang, J., Xia, Y., Tao, D.: VNAS: variational neural architecture search. Int. J. Comput. Vis. 1–25 (2024)
Malinin, A., Gales, M.: Predictive uncertainty estimation via prior networks. In: NeurIPS, vol. 31 (2018)
McMahan, B., Moore, E., et al.: Communication-efficient learning of deep networks from decentralized data. In: AISTATS (2017)
Orlando, J.I., Fu, H., Breda, J.B., et al.: Refuge challenge: a unified framework for evaluating automated methods for glaucoma assessment from fundus photographs. Med. Image Anal. 59, 101570 (2020)
Qu, Z., Li, X., Duan, R., et al.: Generalized federated learning via sharpness aware minimization. In: ICML (2022)
Rehman, Y.A.U., Gao, Y., De Gusmão, P.P.B., et al.: L-DAWA: layer-wise divergence aware weight aggregation in federated self-supervised visual representation learning. In: ICCV, pp. 16464–16473 (2023)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Sensoy, M., Kaplan, L., Kandemir, M.: Evidential deep learning to quantify classification uncertainty. In: NeurIPS, vol. 31 (2018)
Shannon, C.E.: A mathematical theory of communication. Bell Syst. Tech. J. 27(3), 379–423 (1948)
Silva, J., Histace, A., Romain, O., et al.: Toward embedded detection of polyps in WCE images for early diagnosis of colorectal cancer. Int. J. Comput. Assist. Radiol. Surg. 9, 283–293 (2014)
Sivaswamy, J., Krishnadas, S., Chakravarty, A., et al.: A comprehensive retinal image dataset for the assessment of glaucoma from the optic nerve head analysis. JSM Biomed. Imaging Data Papers 2(1), 1004 (2015)
Tajbakhsh, N., Gurudu, S.R., Liang, J.: Automated polyp detection in colonoscopy videos using shape and context information. IEEE Trans. Med. Imaging 35(2), 630–644 (2015)
Tan, Y., Long, G., Liu, L., et al.: Fedproto: federated prototype learning across heterogeneous clients. In: AAAI (2022)
Wang, J., Jin, Y., Wang, L.: Personalizing federated medical image segmentation via local calibration. In: ECCV (2022)
Wang, M., Wang, L., Xu, X., et al.: Federated uncertainty-aware aggregation for fundus diabetic retinopathy staging. arXiv preprint arXiv:2303.13033 (2023)
Wu, N., Yu, L., Yang, X., Cheng, K.T., Yan, Z.: FedIIC: towards robust federated learning for class-imbalanced medical image classification. In: Greenspan, H., et al. (eds.) MICCAI 2023. LNCS, vol. 14221, pp. 692–702. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-43895-0_65
Xie, M., Li, S., Zhang, R., Liu, C.H.: Dirichlet-based uncertainty calibration for active domain adaptation. In: ICLR (2023)
Zhang, L., Wang, X., Yang, D., et al.: Generalizing deep learning for medical image segmentation to unseen domains via deep stacked transformation. IEEE Trans. Med. Imaging 39(7), 2531–2540 (2020)
Zhang, R., Xu, Q., Yao, J., et al.: Federated domain generalization with generalization adjustment. In: CVPR (2023)
Zhou, Q., Zheng, G.: FedContrast-GPA: heterogeneous federated optimization via local contrastive learning and global process-aware aggregation. In: Greenspan, H., et al. (eds.) MICCAI 2023. LNCS, vol. 14221, pp. 660–670. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-43895-0_62
Acknowledgments
This work was supported in part by the National Natural Science Foundation of China under Grants 62171377, in part by Shenzhen Science and Technology Program under Grants JCYJ20220530161616036, in part by the Ningbo Clinical Research Center for Medical Imaging under Grant 2021L003 (Open Project 2022LYKFZD06), and in part by the Innovation Foundation for Master Dissertation of Northwestern Polytechnical University under Grant PF2024013.
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Chen, J., Ma, B., Cui, H., Xia, Y. (2024). FedEvi: Improving Federated Medical Image Segmentation via Evidential Weight Aggregation. In: Linguraru, M.G., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2024. MICCAI 2024. Lecture Notes in Computer Science, vol 15010. Springer, Cham. https://doi.org/10.1007/978-3-031-72117-5_34
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