CE-FI maps heterogeneous model representations to a shared embedding space via unsupervised training on unlabeled data, enabling privacy-preserving federated inference that outperforms solo models on image classification benchmarks.
Towards one-shot federated learning: Advances, chal- lenges, and future directions
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Enabling Federated Inference via Unsupervised Consensus Embedding
CE-FI maps heterogeneous model representations to a shared embedding space via unsupervised training on unlabeled data, enabling privacy-preserving federated inference that outperforms solo models on image classification benchmarks.