Abstract
We introduce similarity weighted aggregation, a principled and efficient method for regularized weight aggregation in federated learning. Our method is adapted to non-IID collaborators and is simultaneously cost-efficient. This is the first method to propose a sliding-window to select the collaborators, to the best of our knowledge. We demonstrate our method on the federate training task of the FeTS 2021 challenge. We proposed two variations coined Similarity Weighted Aggregation (SimAgg) and Regularized Aggregation (RegAgg). SimAgg results on internal validation data demonstrate that the proposed method outperforms the baseline FedAvg. The method SimAgg by our team HT-TUAS won 2nd position on both leaderboards in FeTS2021 challenge. SimAgg is the only method to be among the top performing methods on both the leaderboards, making it robust and reliable to data variations. Our solution is open sourced at: https://github.com/dskhanirfan/FeTS2021
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Annas, G.J.: HIPAA regulations-a new era of medical-record privacy? (2003)
Bakas, S., et al.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-GBM collection. The cancer imaging archive. Nat. Sci. Data 4, 170117 (2017)
Bakas, S., et al.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-LGG collection. Cancer Imaging Archive 286 (2017)
Bakas, S., et al.: Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Sci. Data 4(1), 1–13 (2017)
Beel, J.: Federated meta-learning: democratizing algorithm selection across disciplines and software libraries. Science (AICS) 210, 219 (2018)
Chen, Y., Sun, X., Jin, Y.: Communication-efficient federated deep learning with layerwise asynchronous model update and temporally weighted aggregation. IEEE Trans. Neural Netw. Learn. Syst. 31(10), 4229–4238 (2019)
Corinzia, L., Beuret, A., Buhmann, J.M.: Variational federated multi-task learning. arXiv preprint arXiv:1906.06268 (2019)
Fung, C., Yoon, C.J., Beschastnikh, I.: Mitigating sybils in federated learning poisoning. arXiv preprint arXiv:1808.04866 (2018)
He, C., Annavaram, M., Avestimehr, S.: Group knowledge transfer: federated learning of large CNNs at the edge. arXiv preprint arXiv:2007.14513 (2020)
Jeong, E., Oh, S., Kim, H., Park, J., Bennis, M., Kim, S.L.: Communication-efficient on-device machine learning: Federated distillation and augmentation under non-IID private data. arXiv preprint arXiv:1811.11479 (2018)
Kadhe, S., Rajaraman, N., Koyluoglu, O.O., Ramchandran, K.: FastSecAgg: scalable secure aggregation for privacy-preserving federated learning. arXiv preprint arXiv:2009.11248 (2020)
Kairouz, P., et al.: Advances and open problems in federated learning (2019). https://arxiv.org/abs/1912.04977
Li, T., Sahu, A.K., Zaheer, M., Sanjabi, M., Talwalkar, A., Smith, V.: Federated optimization in heterogeneous networks. arXiv preprint arXiv:1812.06127 (2018)
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)
Nasr, M., Shokri, R., Houmansadr, A.: Comprehensive privacy analysis of deep learning: passive and active white-box inference attacks against centralized and federated learning. In: 2019 IEEE Symposium on Security and Privacy (SP), pp. 739–753. IEEE (2019)
Pati, S., et al.: The federated tumor segmentation (FETS) challenge. arXiv preprint arXiv:2105.05874 (2021)
Reina, G.A., et al.: OpenFL: an open-source framework for federated learning. arXiv preprint arXiv:2105.06413 (2021)
Sadilek, A., et al.: Privacy-first health research with federated learning. medRxiv (2020)
Truex, S., Baracaldo, N., Anwar, A., Steinke, T., Ludwig, H., Zhang, R.: A hybrid approach to privacy-preserving federated learning (2018)
Voigt, P., Von dem Bussche, A.: The EU General Data Protection Regulation (GDPR). A Practical Guide, 1st edn. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-57959-7
Wei, K., et al.: Federated learning with differential privacy: algorithms and performance analysis. IEEE Trans. Inf. Forensics Secur. 15, 3454–3469 (2020)
Xu, J., Glicksberg, B.S., Su, C., Walker, P., Bian, J., Wang, F.: Federated learning for healthcare informatics. J. Healthc. Inform. Res. 5(1), 1–19 (2021)
Yeganeh, Y., Farshad, A., Navab, N., Albarqouni, S.: Inverse distance aggregation for federated learning with non-IID data. In: Albarqouni, S., et al. (eds.) DART/DCL -2020. LNCS, vol. 12444, pp. 150–159. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-60548-3_15
Zhao, Y., Li, M., Lai, L., Suda, N., Civin, D., Chandra, V.: Federated learning with non-IID data. arXiv preprint arXiv:1806.00582 (2018)
Acknowledgements
This work was supported by the Business Finland under Grant 33961/31/2020. We also acknowledge the support and computational resources facilitated by the CSC-Puhti super-computer, a non-profit state enterprise owned by the Finnish state and higher education institutions in Finland.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Khan, M.I., Jafaritadi, M., Alhoniemi, E., Kontio, E., Khan, S.A. (2022). Adaptive Weight Aggregation in Federated Learning for Brain Tumor Segmentation. In: Crimi, A., Bakas, S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2021. Lecture Notes in Computer Science, vol 12963. Springer, Cham. https://doi.org/10.1007/978-3-031-09002-8_40
Download citation
DOI: https://doi.org/10.1007/978-3-031-09002-8_40
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-09001-1
Online ISBN: 978-3-031-09002-8
eBook Packages: Computer ScienceComputer Science (R0)
