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Title: Federated Learning Over Images: Vertical Decompositions and Pre-Trained Backbones Are Difficult to Beat
We carefully evaluate a number of algorithms for learning in a federated environment, and test their utility for a variety of image classification tasks. We consider many issues that have not been adequately considered before: whether learning over data sets that do not have diverse sets of images affects the results; whether to use a pre-trained feature extraction "backbone"; how to evaluate learner performance (we argue that classification accuracy is not enough), among others. Overall, across a wide variety of settings, we find that vertically decomposing a neural network seems to give the best results, and outperforms more standard reconciliation-used methods.  more » « less
Award ID(s):
1918839
NSF-PAR ID:
10497574
Author(s) / Creator(s):
; ; ;
Publisher / Repository:
IEEE
Date Published:
Journal Name:
2023 IEEE/CVF International Conference on Computer Vision (ICCV)
ISBN:
979-8-3503-0718-4
Page Range / eLocation ID:
19328 to 19339
Format(s):
Medium: X
Location:
Paris, France
Sponsoring Org:
National Science Foundation
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