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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 101–125 of 311 papers

TitleStatusHype
Decentralized Personalized Federated Learning—0
DP^2-FedSAM: Enhancing Differentially Private Federated Learning Through Personalized Sharpness-Aware Minimization—0
Federated Learning with Unlabeled Clients: Personalization Can Happen in Low Dimensions—0
FedPAE: Peer-Adaptive Ensemble Learning for Asynchronous and Model-Heterogeneous Federated Learning—0
A Personalized Federated Learning Algorithm: an Application in Anomaly Detection—0
Advances and Challenges in Meta-Learning: A Technical Review—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
Hierarchical Personalized Federated Learning Over Massive Mobile Edge Computing Networks—0
FedSheafHN: Personalized Federated Learning on Graph-structured Data—0
Federated Learning of Shareable Bases for Personalization-Friendly Image Classification—0
Decentralized Directed Collaboration for Personalized Federated Learning—0
Federated Learning for Chronic Obstructive Pulmonary Disease Classification with Partial Personalized Attention Mechanism—0
DA-PFL: Dynamic Affinity Aggregation for Personalized Federated Learning—0
FedSub: Introducing class-aware Subnetworks Fusion to Enhance Personalized Federated Learning in Ubiquitous Systems—0
A Parameter Aggregation Strategy on Personalized Federated Learning—0
Group privacy for personalized federated learning—0
A novel parameter decoupling approach of personalized federated learning for image analysis—0
FedD2S: Personalized Data-Free Federated Knowledge Distillation—0
Find Your Friends: Personalized Federated Learning with the Right Collaborators—0
Federated Asymptotics: a model to compare federated learning algorithms—0
Addressing Data Heterogeneity in Federated Learning with Adaptive Normalization-Free Feature Recalibration—0
Friends in Unexpected Places: Enhancing Local Fairness in Federated Learning through Clustering—0
Formal Logic Enabled Personalized Federated Learning Through Property Inference—0
FedCRL: Personalized Federated Learning with Contrastive Shared Representations for Label Heterogeneity in Non-IID Data—0
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