SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 251–275 of 311 papers

TitleStatusHype
Federated Learning with Unlabeled Clients: Personalization Can Happen in Low Dimensions—0
Federated Neural Compression Under Heterogeneous Data—0
FedGlu: A personalized federated learning-based glucose forecasting algorithm for improved performance in glycemic excursion regions—0
FedGradNorm: Personalized Federated Gradient-Normalized Multi-Task Learning—0
FedHome: Cloud-Edge based Personalized Federated Learning for In-Home Health Monitoring—0
FediOS: Decoupling Orthogonal Subspaces for Personalization in Feature-skew Federated Learning—0
pFedLoRA: Model-Heterogeneous Personalized Federated Learning with LoRA Tuning—0
FedMCSA: Personalized Federated Learning via Model Components Self-Attention—0
FedMoE: Personalized Federated Learning via Heterogeneous Mixture of Experts—0
FedPAE: Peer-Adaptive Ensemble Learning for Asynchronous and Model-Heterogeneous Federated Learning—0
FedSelect: Customized Selection of Parameters for Fine-Tuning during Personalized Federated Learning—0
FedSheafHN: Personalized Federated Learning on Graph-structured Data—0
FedSI: Federated Subnetwork Inference for Efficient Uncertainty Quantification—0
FedSPD: A Soft-clustering Approach for Personalized Decentralized Federated Learning—0
FedSub: Introducing class-aware Subnetworks Fusion to Enhance Personalized Federated Learning in Ubiquitous Systems—0
Find Your Friends: Personalized Federated Learning with the Right Collaborators—0
Federated Asymptotics: a model to compare federated learning algorithms—0
Formal Logic Enabled Personalized Federated Learning Through Property Inference—0
Generalized and Personalized Federated Learning with Foundation Models via Orthogonal Transformations—0
Group Personalized Federated Learning—0
Group privacy for personalized federated learning—0
Heterogeneity-aware Personalized Federated Learning via Adaptive Dual-Agent Reinforcement Learning—0
Hierarchical Over-the-Air FedGradNorm—0
Hierarchical Personalized Federated Learning Over Massive Mobile Edge Computing Networks—0
How To Prevent the Poor Performance Clients for Personalized Federated Learning?—0
Show:102550
← PrevPage 11 of 13Next →

No leaderboard results yet.