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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 1–25 of 311 papers

TitleStatusHype
PFLlib: A Beginner-Friendly and Comprehensive Personalized Federated Learning Library and BenchmarkCode4
GPFL: Simultaneously Learning Global and Personalized Feature Information for Personalized Federated LearningCode4
FedCP: Separating Feature Information for Personalized Federated Learning via Conditional PolicyCode4
Calibre: Towards Fair and Accurate Personalized Federated Learning with Self-Supervised LearningCode3
ZooPFL: Exploring Black-box Foundation Models for Personalized Federated LearningCode2
Adaptive Personalized Federated LearningCode2
FedModule: A Modular Federated Learning FrameworkCode2
FedSelect: Personalized Federated Learning with Customized Selection of Parameters for Fine-TuningCode1
Ditto: Fair and Robust Federated Learning Through PersonalizationCode1
FedSSA: Semantic Similarity-based Aggregation for Efficient Model-Heterogeneous Personalized Federated LearningCode1
Bold but Cautious: Unlocking the Potential of Personalized Federated Learning through Cautiously Aggressive CollaborationCode1
Federated Multi-Task Learning under a Mixture of DistributionsCode1
Adaptive Intrusion Detection in the Networking of Large-Scale LANs with Segmented Federated LearningCode1
FedPerfix: Towards Partial Model Personalization of Vision Transformers in Federated LearningCode1
FedAS: Bridging Inconsistency in Personalized Federated LearningCode1
Efficient Split-Mix Federated Learning for On-Demand and In-Situ CustomizationCode1
FedClassAvg: Local Representation Learning for Personalized Federated Learning on Heterogeneous Neural NetworksCode1
Confidence-aware Personalized Federated Learning via Variational Expectation MaximizationCode1
Dual‑detector Re‑optimization for Federated Weakly Supervised Video Anomaly Detection Via Adaptive Dynamic Recursive MappingCode1
Efficient Personalized Federated Learning via Sparse Model-AdaptationCode1
Exploiting Shared Representations for Personalized Federated LearningCode1
FedALA: Adaptive Local Aggregation for Personalized Federated LearningCode1
Adaptive Test-Time Personalization for Federated LearningCode1
Personalized Federated Learning with Adaptive Batchnorm for HealthcareCode1
Decoupling General and Personalized Knowledge in Federated Learning via Additive and Low-Rank DecompositionCode1
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