FMTC learns personalized clustering models on clients and uses server-side tensor low-rank regularization to capture shared structure across heterogeneous clients in a privacy-preserving federated setting.
Communication-efficient learning of deep networks from decentralized data
2 Pith papers cite this work. Polarity classification is still indexing.
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Proposes budget-constrained adaptive noise allocation for privacy-preserving federated radio map learning with RadioUNet, claiming better privacy and reconstruction quality than uniform or static baselines under matched budgets.
citing papers explorer
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Federated Multi-Task Clustering
FMTC learns personalized clustering models on clients and uses server-side tensor low-rank regularization to capture shared structure across heterogeneous clients in a privacy-preserving federated setting.
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Privacy-Preserving Federated Radio Map Learning for Wireless Digital Twins via Adaptive Noise Allocation
Proposes budget-constrained adaptive noise allocation for privacy-preserving federated radio map learning with RadioUNet, claiming better privacy and reconstruction quality than uniform or static baselines under matched budgets.