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This paper presents FedPIDAvg, the winning submission to the Federated TumorSegmentation Challenge 2022 (FETS22). Inspired by FedCostWAvg, our winningcontribution to FETS21, we contribute an improved aggregation strategy forfederated and collaborative learning. FedCostWAvg is a weighted averagingmethod that not only considers the number of training samples of each clusterbut also the size of the drop of the respective cost function in the lastfederated round. This can be interpreted as the derivative part of a PIDcontroller (proportional-integral-derivative controller). In FedPIDAvg, wefurther add the missing integral term. Another key challenge was the vastlyvarying size of data samples per center. We addressed this by modeling the datacenter sizes as following a Poisson distribution and choosing the trainingiterations per center accordingly. Our method outperformed all othersubmissions.

FedPIDAvg: A PID controller inspired aggregation method for Federated Learning