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Personalized Federated Learning

The federated learning setup presents numerous challenges including data heterogeneity (differences in data distribution), device heterogeneity (in terms of computation capabilities, network connection, etc.), and communication efficiency. Especially data heterogeneity makes it hard to learn a single shared global model that applies to all clients. To overcome these issues, Personalized Federated Learning (PFL) aims to personalize the global model for each client in the federation.

Papers

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Showing 176–200 of 311 papers

TitleStatusHype
Personalized Over-the-Air Federated Learning with Personalized Reconfigurable Intelligent Surfaces—0
Communication-Efficient Personalized Federated Learning for Speech-to-Text Tasks—0
FedLoGe: Joint Local and Generic Federated Learning under Long-tailed DataCode0
Personalized Federated Learning of Probabilistic Models: A PAC-Bayesian Approach—0
Learn What You Need in Personalized Federated LearningCode0
Formal Logic Enabled Personalized Federated Learning Through Property Inference—0
Personalized Federated Learning with Contextual Modulation and Meta-LearningCode0
Personalized Federated Learning with Attention-based Client Selection—0
Fed-CO2: Cooperation of Online and Offline Models for Severe Data Heterogeneity in Federated LearningCode0
Fed-QSSL: A Framework for Personalized Federated Learning under Bitwidth and Data HeterogeneityCode0
Dirichlet-based Uncertainty Quantification for Personalized Federated Learning with Improved Posterior Networks—0
Factor-Assisted Federated Learning for Personalized Optimization with Heterogeneous Data—0
FediOS: Decoupling Orthogonal Subspaces for Personalization in Feature-skew Federated Learning—0
Fault Detection in Telecom Networks using Bi-level Federated Graph Neural Networks—0
pFedES: Model Heterogeneous Personalized Federated Learning with Feature Extractor Sharing—0
Personalized Federated Learning via ADMM with Moreau Envelope—0
Efficient Cluster Selection for Personalized Federated Learning: A Multi-Armed Bandit Approach—0
Contextual Stochastic Bilevel Optimization—0
PPFL: A Personalized Federated Learning Framework for Heterogeneous Population—0
pFedLoRA: Model-Heterogeneous Personalized Federated Learning with LoRA Tuning—0
Bayesian Personalized Federated Learning with Shared and Personalized Uncertainty Representations—0
Advancing Personalized Federated Learning: Group Privacy, Fairness, and Beyond—0
PFL-GAN: When Client Heterogeneity Meets Generative Models in Personalized Federated Learning—0
Aggregating Intrinsic Information to Enhance BCI Performance through Federated LearningCode0
UPFL: Unsupervised Personalized Federated Learning towards New ClientsCode0
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