  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:
\url{https://github.com/dskhanirfan/FeTS2022}
