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Efficient Image Representation Learning with Federated Sampled Softmax
[article]
2022
arXiv
pre-print
Learning image representations on decentralized data can bring many benefits in cases where data cannot be aggregated across data silos. Softmax cross entropy loss is highly effective and commonly used for learning image representations. Using a large number of classes has proven to be particularly beneficial for the descriptive power of such representations in centralized learning. However, doing so on decentralized data with Federated Learning is not straightforward as the demand on FL
arXiv:2203.04888v1
fatcat:rindqzo2pbg4bepb4ldm673x3a