… text, to propose a personalization mechanism based on local … generalization bounds for
the proposed approach in the case of binary classification, and we show on a suite of federated …

… In this section, we propose a personalization approach for federated learning and analyze
its statistical properties. Following the statistical learning theory, in a federated learning setting …

… federated learning with Gaussian processes Now we describe our approach for applying
personalized federated learning … how to use Gibbs sampling to learn the NN parameters. Then, …

… Federated learning faces huge challenges from model … proposes a novel personalized
federated learning method via Bayesian … A natural question is can we design a federated learning …

… To quantitatively study the generalization-personalization trade-off, we introduce the ‘generalization
error’ measure and prove that the proposed CGPFL can achieve a better trade-off …

… We provide generalization bounds and communication-efficient algorithms for all of the above
methods. We show that the above three methods has small communication bottleneck and …

… PERADA achieves high generalization by regularizing each client’s personalized adapter
… generalized information from all clients. Theoretically, we provide generalization bounds of …

… samples, while keeping local generalization errors low. This raises the … personalization and
coordination, and how can it be achieved? We formulate the personalized federated learning …

… propose a novel personalized federated learning framework, … (Non-IID) in the federated
setting. To the best of our … clustered generalization (CG) for personalized federated learning and …

… The abundance of data generated in a massive number of hand-held devices these days
has stimulated the development of Federated learning (FL) [1]. The setting of FL is a network of …