PINA improves accuracy in differentially private clustered federated learning by an average of 2.9% using privacy-preserving LoRA sketches for cluster initialization and normality-driven aggregation.
Clustered feder- ated learning with adaptive local differential privacy on hetero- geneous IoT data
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Differentially Private Clustered Federated Learning with Privacy-Preserving Initialization and Normality-Driven Aggregation
PINA improves accuracy in differentially private clustered federated learning by an average of 2.9% using privacy-preserving LoRA sketches for cluster initialization and normality-driven aggregation.