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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
Advances and Challenges in Meta-Learning: A Technical Review—0
DP^2-FedSAM: Enhancing Differentially Private Federated Learning Through Personalized Sharpness-Aware Minimization—0
FedMoE: Personalized Federated Learning via Heterogeneous Mixture of Experts—0
FedPAE: Peer-Adaptive Ensemble Learning for Asynchronous and Model-Heterogeneous Federated Learning—0
Federated Learning of Shareable Bases for Personalization-Friendly Image Classification—0
Decentralized Directed Collaboration for Personalized Federated Learning—0
Bayesian Personalized Federated Learning with Shared and Personalized Uncertainty Representations—0
FedSelect: Customized Selection of Parameters for Fine-Tuning during Personalized Federated Learning—0
Federated Learning for Chronic Obstructive Pulmonary Disease Classification with Partial Personalized Attention Mechanism—0
FedSheafHN: Personalized Federated Learning on Graph-structured Data—0
DA-PFL: Dynamic Affinity Aggregation for Personalized Federated Learning—0
A Parameter Aggregation Strategy on Personalized Federated Learning—0
Multi-level Personalized Federated Learning on Heterogeneous and Long-Tailed Data—0
A novel parameter decoupling approach of personalized federated learning for image analysis—0
FedSub: Introducing class-aware Subnetworks Fusion to Enhance Personalized Federated Learning in Ubiquitous Systems—0
FedD2S: Personalized Data-Free Federated Knowledge Distillation—0
Addressing Data Heterogeneity in Federated Learning with Adaptive Normalization-Free Feature Recalibration—0
FedCRL: Personalized Federated Learning with Contrastive Shared Representations for Label Heterogeneity in Non-IID Data—0
Cross-Fusion Rule for Personalized Federated Learning—0
Find Your Friends: Personalized Federated Learning with the Right Collaborators—0
Federated Asymptotics: a model to compare federated learning algorithms—0
ActPerFL: Active Personalized Federated Learning—0
New Metrics to Evaluate the Performance and Fairness of Personalized Federated Learning—0
Formal Logic Enabled Personalized Federated Learning Through Property Inference—0
Exploiting Personalized Invariance for Better Out-of-distribution Generalization in Federated Learning—0
Generalized and Personalized Federated Learning with Foundation Models via Orthogonal Transformations—0
On Heterogeneously Distributed Data, Sparsity Matters—0
Convergence-Privacy-Fairness Trade-Off in Personalized Federated Learning—0
A Novel Algorithm for Personalized Federated Learning: Knowledge Distillation with Weighted Combination Loss—0
FedCALM: Conflict-aware Layer-wise Mitigation for Selective Aggregation in Deeper Personalized Federated Learning—0
FedASTA: Federated adaptive spatial-temporal attention for traffic flow prediction—0
Convergence of First-Order Algorithms for Meta-Learning with Moreau Envelopes—0
Mobilizing Personalized Federated Learning in Infrastructure-Less and Heterogeneous Environments via Random Walk Stochastic ADMM—0
MH-pFLID: Model Heterogeneous personalized Federated Learning via Injection and Distillation for Medical Data Analysis—0
FedAPA: Server-side Gradient-Based Adaptive Personalized Aggregation for Federated Learning on Heterogeneous Data—0
Mitigating Biases in Student Performance Prediction via Attention-Based Personalized Federated Learning—0
An Optimal Transport Approach to Personalized Federated Learning—0
Contextual Stochastic Bilevel Optimization—0
MH-pFLGB: Model Heterogeneous personalized Federated Learning via Global Bypass for Medical Image Analysis—0
Mitigating Membership Inference Vulnerability in Personalized Federated Learning—0
How to Backdoor HyperNetwork in Personalized Federated Learning?—0
Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data—0
Communication-Efficient Personalized Federated Learning for Speech-to-Text Tasks—0
Look Back for More: Harnessing Historical Sequential Updates for Personalized Federated Adapter Tuning—0
Integrating Personalized Federated Learning with Control Systems for Enhanced Performance—0
Lower Bounds and Optimal Algorithms for Personalized Federated Learning—0
Influence-oriented Personalized Federated Learning—0
Lurking in the shadows: Unveiling Stealthy Backdoor Attacks against Personalized Federated Learning—0
Robustness and Personalization in Federated Learning: A Unified Approach via Regularization—0
Client-supervised Federated Learning: Towards One-model-for-all Personalization—0
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