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Found 5 papers, 1 papers with code
Hierarchical Bayes Approach to Personalized Federated Unsupervised Learning
Though there has been a plethora of algorithms proposed for personalized supervised learning, discovering the structure of local data through personalized unsupervised learning is less explored.
MADA: Meta-Adaptive Optimizers through hyper-gradient Descent
Since Adam was introduced, several novel adaptive optimizers for deep learning have been proposed.
A Generative Framework for Personalized Learning and Estimation: Theory, Algorithms, and Privacy
In this work, we begin with a generative framework that could potentially unify several different algorithms as well as suggest new algorithms.
QuPeD: Quantized Personalization via Distillation with Applications to Federated Learning
In this work, we introduce a \textit{quantized} and \textit{personalized} FL algorithm QuPeD that facilitates collective (personalized model compression) training via \textit{knowledge distillation} (KD) among clients who have access to heterogeneous data and resources.
QuPeL: Quantized Personalization with Applications to Federated Learning
When each client participating in the (federated) learning process has different requirements of the quantized model (both in value and precision), we formulate a quantized personalization framework by introducing a penalty term for local client objectives against a globally trained model to encourage collaboration.
