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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 101–150 of 311 papers

TitleStatusHype
FedSheafHN: Personalized Federated Learning on Graph-structured Data—0
FedASTA: Federated adaptive spatial-temporal attention for traffic flow prediction—0
MH-pFLID: Model Heterogeneous personalized Federated Learning via Injection and Distillation for Medical Data Analysis—0
Multi-level Personalized Federated Learning on Heterogeneous and Long-Tailed Data—0
FedCRL: Personalized Federated Learning with Contrastive Shared Representations for Label Heterogeneity in Non-IID Data—0
Personalized Federated Learning via Sequential Layer Expansion in Representation Learning—0
pFedAFM: Adaptive Feature Mixture for Batch-Level Personalization in Heterogeneous Federated LearningCode0
FedSI: Federated Subnetwork Inference for Efficient Uncertainty Quantification—0
Decentralized Personalized Federated Learning based on a Conditional Sparse-to-Sparser SchemeCode0
Personalized Federated Learning via StackingCode0
Personalized Federated Learning for Spatio-Temporal Forecasting: A Dual Semantic Alignment-Based Contrastive Approach—0
FedSelect: Personalized Federated Learning with Customized Selection of Parameters for Fine-TuningCode1
Client-supervised Federated Learning: Towards One-model-for-all Personalization—0
Loop Improvement: An Efficient Approach for Extracting Shared Features from Heterogeneous Data without Central ServerCode0
FedSPU: Personalized Federated Learning for Resource-constrained Devices with Stochastic Parameter UpdateCode0
DA-PFL: Dynamic Affinity Aggregation for Personalized Federated Learning—0
RobWE: Robust Watermark Embedding for Personalized Federated Learning Model Ownership Protection—0
Trustworthy Personalized Bayesian Federated Learning via Posterior Fine-Tune—0
Bayesian Neural Network For Personalized Federated Learning Parameter Selection—0
FedD2S: Personalized Data-Free Federated Knowledge Distillation—0
Prompt-based Personalized Federated Learning for Medical Visual Question Answering—0
Personalized Federated Learning for Statistical Heterogeneity—0
pFedMoE: Data-Level Personalization with Mixture of Experts for Model-Heterogeneous Personalized Federated LearningCode0
Spectral Co-Distillation for Personalized Federated LearningCode0
Rethinking Personalized Federated Learning with Clustering-based Dynamic Graph Propagation—0
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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