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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 201–250 of 311 papers

TitleStatusHype
WAFFLE: Weighted Averaging for Personalized Federated Learning—0
WarmFed: Federated Learning with Warm-Start for Globalization and Personalization Via Personalized Diffusion Models—0
Which Client is Reliable?: A Reliable and Personalized Prompt-based Federated Learning for Medical Image Question Answering—0
Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data—0
Look Back for More: Harnessing Historical Sequential Updates for Personalized Federated Adapter Tuning—0
Lower Bounds and Optimal Algorithms for Personalized Federated Learning—0
Lurking in the shadows: Unveiling Stealthy Backdoor Attacks against Personalized Federated Learning—0
MH-pFLGB: Model Heterogeneous personalized Federated Learning via Global Bypass for Medical Image Analysis—0
MH-pFLID: Model Heterogeneous personalized Federated Learning via Injection and Distillation for Medical Data Analysis—0
Mitigating Biases in Student Performance Prediction via Attention-Based Personalized Federated Learning—0
Mitigating Membership Inference Vulnerability in Personalized Federated Learning—0
Mobilizing Personalized Federated Learning in Infrastructure-Less and Heterogeneous Environments via Random Walk Stochastic ADMM—0
How to Backdoor HyperNetwork in Personalized Federated Learning?—0
Multi-Layer Personalized Federated Learning for Mitigating Biases in Student Predictive Analytics—0
Multi-level Personalized Federated Learning on Heterogeneous and Long-Tailed Data—0
New Metrics to Evaluate the Performance and Fairness of Personalized Federated Learning—0
On Data Efficiency of Meta-learning—0
On Heterogeneously Distributed Data, Sparsity Matters—0
PartialFed: Cross-Domain Personalized Federated Learning via Partial Initialization—0
Partially Personalized Federated Learning: Breaking the Curse of Data Heterogeneity—0
PerFED-GAN: Personalized Federated Learning via Generative Adversarial Networks—0
PersA-FL: Personalized Asynchronous Federated Learning—0
Personalization Disentanglement for Federated Learning: An explainable perspective—0
Personalized Federated Domain Adaptation for Item-to-Item Recommendation—0
Personalized Federated Hypernetworks for Privacy Preservation in Multi-Task Reinforcement Learning—0
Personalized Federated Learning: A Meta-Learning Approach—0
Personalized federated learning based on feature fusion—0
Personalized Federated Learning for Intelligent IoT Applications: A Cloud-Edge based Framework—0
Personalized Federated Learning for Heterogeneous Clients with Clustered Knowledge Transfer—0
Personalized Federated Learning for Statistical Heterogeneity—0
Personalized Federated Learning for Generative AI-Assisted Semantic Communications—0
Personalized Federated Learning for Egocentric Video Gaze Estimation with Comprehensive Parameter Frezzing—0
Personalized Federated Learning for Cellular VR: Online Learning and Dynamic Caching—0
Personalized Federated Learning for Cross-view Geo-localization—0
Personalized Federated Learning for improving radar based precipitation nowcasting on heterogeneous areas—0
Personalized Federated Learning for Multi-task Fault Diagnosis of Rotating Machinery—0
Personalized Federated Learning for Spatio-Temporal Forecasting: A Dual Semantic Alignment-Based Contrastive Approach—0
Personalized Federated Learning of Probabilistic Models: A PAC-Bayesian Approach—0
Personalized Federated Learning of Driver Prediction Models for Autonomous Driving—0
Personalized Federated Learning over non-IID Data for Indoor Localization—0
Personalized Federated Learning Techniques: Empirical Analysis—0
Personalized Federated Learning under Model Dissimilarity Constraints—0
Personalized Federated Learning via Sequential Layer Expansion in Representation Learning—0
Personalized Federated Learning via Active Sampling—0
Personalized Federated Learning via ADMM with Moreau Envelope—0
Personalized Federated Learning via Amortized Bayesian Meta-Learning—0
Personalized Federated Learning via Backbone Self-Distillation—0
Personalized Federated Learning via Convex Clustering—0
Personalized Federated Learning via Dual-Prompt Optimization and Cross Fusion—0
Fusion of Global and Local Knowledge for Personalized Federated LearningCode0
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