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.
The spe- cific condition is typically of the formρ > cL f for some constantc, ensuring that the quadratic penalty dominates any potential increase caused by non-convexity
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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.