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Adaptive Weight Aggregation in Federated Learning for Brain Tumor Segmentation

Part of the Lecture Notes in Computer Science book series (LNCS,volume 12963)


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:


  • Brain tumors
  • Cancer
  • Collaborative learning
  • Federated learning
  • FeTS challenge
  • Lesion segmentation
  • Weight aggregation

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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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Correspondence to Muhammad Irfan Khan .

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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.

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  • Print ISBN: 978-3-031-09001-1

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