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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 126–150 of 311 papers

TitleStatusHype
Viewport Prediction, Bitrate Selection, and Beamforming Design for THz-Enabled 360° Video Streaming—0
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
Learn What You Need in Personalized Federated LearningCode0
Personalized Federated Learning of Probabilistic Models: A PAC-Bayesian Approach—0
Formal Logic Enabled Personalized Federated Learning Through Property Inference—0
FedAS: Bridging Inconsistency in Personalized Federated LearningCode1
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
FedSSA: Semantic Similarity-based Aggregation for Efficient Model-Heterogeneous Personalized Federated LearningCode1
PFLlib: A Beginner-Friendly and Comprehensive Personalized Federated Learning Library and BenchmarkCode4
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
Personalized Federated Learning via ADMM with Moreau Envelope—0
pFedES: Model Heterogeneous Personalized Federated Learning with Feature Extractor Sharing—0
Efficient Cluster Selection for Personalized Federated Learning: A Multi-Armed Bandit Approach—0
Adaptive Test-Time Personalization for Federated LearningCode1
Contextual Stochastic Bilevel Optimization—0
Unlocking the Potential of Prompt-Tuning in Bridging Generalized and Personalized Federated LearningCode1
PPFL: A Personalized Federated Learning Framework for Heterogeneous Population—0
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