Abstract
In federated learning (FL), the global model at the server requires an efficient mechanism for weight aggregation and a systematic strategy for collaboration selection to manage and optimize communication payload. We introduce a practical and cost-efficient method for regularized weight aggregation and propose a laborsaving technique to select collaborators per round. We illustrate the performance of our method, regularized similarity weight aggregation (RegSimAgg), on the Federated Tumor Segmentation (FeTS) 2022 challenge’s federated training (weight aggregation) problem. Our scalable approach is principled, frugal, and suitable for heterogeneous non-IID collaborators. Using FeTS2021 evaluation criterion, our proposed algorithm RegSimAgg stands at 3rd position in the final rankings of FeTS2022 challenge in the weight aggregation task. Our solution is open sourced at: https://github.com/dskhanirfan/FeTS2022.
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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.
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Khan, M.I., Azeem, M.A., Alhoniemi, E., Kontio, E., Khan, S.A., Jafaritadi, M. (2023). Regularized Weight Aggregation in Networked Federated Learning for Glioblastoma Segmentation. In: Bakas, S., et al. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2022. Lecture Notes in Computer Science, vol 14092. Springer, Cham. https://doi.org/10.1007/978-3-031-44153-0_12
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