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}

该研究提出了一种为联邦学习中的模型权重聚合和协作选择问题提供高效解决方案的方法，名为 RegSimAgg，该方法使用规范化相似度作为惩罚项，结合特定的协作选择方法，能够在非同质协作者间实现高效的模型协作。该方法在 Federated Tumor Segmentation (FeTS) 2022 挑战赛上的结果表明，该方法在权重聚合任务中可以取得较好的效果。

网络联合学习中正则化权重聚合用于脑胶质母细胞瘤分割