This paper presents FedPIDAvg, the winning submission to the Federated Tumor Segmentation Challenge 2022 (FETS22). Inspired by FedCostWAvg, our winning contribution to FETS21, we contribute an improved aggregation strategy for federated and collaborative learning. FedCostWAvg is a weighted averaging method that not only considers the number of training samples of each cluster but also the size of the drop of the respective cost function in the last federated round. This can be interpreted as the derivative part of a PID controller (proportional-integral-derivative controller). In FedPIDAvg, we further add the missing integral term. Another key challenge was the vastly varying size of data samples per center. We addressed this by modeling the data center sizes as following a Poisson distribution and choosing the training iterations per center accordingly. Our method outperformed all other submissions.

翻译：本文介绍了FedPIDAvg，这是在2022年联邦肿瘤分割挑战赛（FETS22）中获胜的提交方案。受我们在FETS21中获奖方案FedCostWAvg的启发，我们提出了一种改进的联邦与协作学习聚合策略。FedCostWAvg是一种加权平均方法，不仅考虑每个集群的训练样本数量，还考虑最近联邦轮次中各自代价函数下降的幅度。这可被解释为PID控制器（比例-积分-微分控制器）的微分部分。在FedPIDAvg中，我们进一步补充了缺失的积分项。另一个关键挑战是各中心数据样本规模差异巨大。我们通过将数据中心规模建模为服从泊松分布并据此选择每个中心的训练迭代次数来解决这一问题。我们的方法优于所有其他提交方案。