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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 26–50 of 311 papers

TitleStatusHype
Personalized Federated Learning using HypernetworksCode1
Adaptive Intrusion Detection in the Networking of Large-Scale LANs with Segmented Federated LearningCode1
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
Personalized Federated Learning with Feature Alignment and Classifier CollaborationCode1
Confidence-aware Personalized Federated Learning via Variational Expectation MaximizationCode1
Efficient Split-Mix Federated Learning for On-Demand and In-Situ CustomizationCode1
Dual‑detector Re‑optimization for Federated Weakly Supervised Video Anomaly Detection Via Adaptive Dynamic Recursive MappingCode1
An Empirical Study of Personalized Federated LearningCode1
A New Look and Convergence Rate of Federated Multi-Task Learning with Laplacian RegularizationCode1
Adaptive Test-Time Personalization for Federated LearningCode1
FedALA: Adaptive Local Aggregation for Personalized Federated LearningCode1
Exploiting Shared Representations for Personalized Federated LearningCode1
FedAS: Bridging Inconsistency in Personalized Federated LearningCode1
FedClassAvg: Local Representation Learning for Personalized Federated Learning on Heterogeneous Neural NetworksCode1
Intrusion Detection with Segmented Federated Learning for Large-Scale Multiple LANsCode1
Efficient Personalized Federated Learning via Sparse Model-AdaptationCode1
Ditto: Fair and Robust Federated Learning Through PersonalizationCode1
Personalized Federated Learning with Adaptive Batchnorm for HealthcareCode1
Federated Multi-Task Learning under a Mixture of DistributionsCode1
Decoupling General and Personalized Knowledge in Federated Learning via Additive and Low-Rank DecompositionCode1
A Comprehensive View of Personalized Federated Learning on Heterogeneous Clinical DatasetsCode1
FedPerfix: Towards Partial Model Personalization of Vision Transformers in Federated LearningCode1
DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse TrainingCode1
Unlocking the Potential of Prompt-Tuning in Bridging Generalized and Personalized Federated LearningCode1
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