ARIA: On the Interaction Between Architectures, Initialization and Aggregation Methods for Federated Visual Classification
arxiv(2023)
摘要
Federated Learning (FL) is a collaborative training paradigm that allows for
privacy-preserving learning of cross-institutional models by eliminating the
exchange of sensitive data and instead relying on the exchange of model
parameters between the clients and a server. Despite individual studies on how
client models are aggregated, and, more recently, on the benefits of ImageNet
pre-training, there is a lack of understanding of the effect the architecture
chosen for the federation has, and of how the aforementioned elements
interconnect. To this end, we conduct the first joint
ARchitecture-Initialization-Aggregation study and benchmark ARIAs across a
range of medical image classification tasks. We find that, contrary to current
practices, ARIA elements have to be chosen together to achieve the best
possible performance. Our results also shed light on good choices for each
element depending on the task, the effect of normalisation layers, and the
utility of SSL pre-training, pointing to potential directions for designing
FL-specific architectures and training pipelines.
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