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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 226–250 of 311 papers

TitleStatusHype
Fusion of Global and Local Knowledge for Personalized Federated LearningCode0
FedABC: Targeting Fair Competition in Personalized Federated Learning—0
Cross-Fusion Rule for Personalized Federated Learning—0
Revisiting Personalized Federated Learning: Robustness Against Backdoor Attacks—0
The Best of Both Worlds: Accurate Global and Personalized Models through Federated Learning with Data-Free Hyper-Knowledge Distillation—0
Convergence of First-Order Algorithms for Meta-Learning with Moreau Envelopes—0
Reliable and Interpretable Personalized Federated Learning—0
How To Prevent the Poor Performance Clients for Personalized Federated Learning?—0
Personalized Semantics Excitation for Federated Image Classification—0
Hierarchical Over-the-Air FedGradNorm—0
Multi-Layer Personalized Federated Learning for Mitigating Biases in Student Predictive Analytics—0
PGFed: Personalize Each Client's Global Objective for Federated LearningCode0
Flow: Per-Instance Personalized Federated Learning Through Dynamic RoutingCode0
Exploiting Personalized Invariance for Better Out-of-distribution Generalization in Federated Learning—0
Personalized Federated Learning with Hidden Information on Personalized Prior—0
Personalized Federated Learning for Multi-task Fault Diagnosis of Rotating Machinery—0
Personalized Federated Learning with Multi-branch Architecture—0
Federated Learning for Chronic Obstructive Pulmonary Disease Classification with Partial Personalized Attention Mechanism—0
Prototype-Based Layered Federated Cross-Modal Hashing—0
Personalized Federated Learning via Heterogeneous Modular NetworksCode0
Personalized Federated Hypernetworks for Privacy Preservation in Multi-Task Reinforcement Learning—0
Find Your Friends: Personalized Federated Learning with the Right Collaborators—0
Group Personalized Federated Learning—0
PersA-FL: Personalized Asynchronous Federated Learning—0
Semi-Synchronous Personalized Federated Learning over Mobile Edge Networks—0
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