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FedUHD: Unsupervised Federated Learning using In-Memory Hyperdimensional Computing
Unsupervised federated learning (UFL) enables privacy-preserving distributed training without data labeling, yet practical deployment remains challenging due to non-IID data, high computational and communication costs at edge devices, and sensitivity to communication noise. We propose FedUHD, the fi
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