FedPIDAvg: A PID controller inspired aggregation method for Federated Learning
This work addresses aggregation challenges in federated learning for medical imaging, but it is incremental as it builds on a previous winning method.
The paper tackles the problem of improving aggregation strategies in federated learning, particularly for medical imaging with varying data sizes across centers, by introducing FedPIDAvg, which adds an integral term to a PID controller-inspired method and models data center sizes with a Poisson distribution, resulting in it winning the Federated Tumor Segmentation Challenge 2022.
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.