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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 201–225 of 311 papers

TitleStatusHype
Reliable and Interpretable Personalized Federated Learning—0
How To Prevent the Poor Performance Clients for Personalized Federated Learning?—0
Hierarchical Over-the-Air FedGradNorm—0
Tackling Data Heterogeneity in Federated Learning with Class PrototypesCode1
Multi-Layer Personalized Federated Learning for Mitigating Biases in Student Predictive Analytics—0
PGFed: Personalize Each Client's Global Objective for Federated LearningCode0
FedALA: Adaptive Local Aggregation for Personalized Federated LearningCode1
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
FedTP: Federated Learning by Transformer PersonalizationCode1
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
FedClassAvg: Local Representation Learning for Personalized Federated Learning on Heterogeneous Neural NetworksCode1
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
Blinder: End-to-end Privacy Protection in Sensing Systems via Personalized Federated LearningCode0
Characterizing Internal Evasion Attacks in Federated LearningCode1
Personalized Federated Learning with Communication Compression—0
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