142 citations · 615 across the 96 of their papers we have counts for
4 papers · 1 filter
FedP3: Federated Personalized and Privacy-friendly Network Pruning under Model Heterogeneity
Kai Yi, Nidham Gazagnadou, Peter Richtárik +1
The interest in federated learning has surged in recent research due to its unique ability to train a global model using privacy-secured information held locally on each client. Th…
DiffImpute: Tabular Data Imputation With Denoising Diffusion Probabilistic Model
Yizhu Wen, Kai Yi, Jing Ke +1
Tabular data plays a crucial role in various domains but often suffers from missing values, thereby curtailing its potential utility. Traditional imputation techniques frequently y…
Explicit Personalization and Local Training: Double Communication Acceleration in Federated Learning
Kai Yi, Laurent Condat, Peter Richtárik
Federated Learning is an evolving machine learning paradigm, in which multiple clients perform computations based on their individual private data, interspersed by communication wi…
Variance Reduced ProxSkip: Algorithm, Theory and Application to Federated Learning
Grigory Malinovsky, Kai Yi, Peter Richtárik
We study distributed optimization methods based on the {\em local training (LT)} paradigm: achieving communication efficiency by performing richer local gradient-based training on…