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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 41–50 of 311 papers

TitleStatusHype
FedTP: Federated Learning by Transformer PersonalizationCode1
A New Look and Convergence Rate of Federated Multi-Task Learning with Laplacian RegularizationCode1
Dual‑detector Re‑optimization for Federated Weakly Supervised Video Anomaly Detection Via Adaptive Dynamic Recursive MappingCode1
Unlocking the Potential of Prompt-Tuning in Bridging Generalized and Personalized Federated LearningCode1
Intrusion Detection with Segmented Federated Learning for Large-Scale Multiple LANsCode1
Decoupling General and Personalized Knowledge in Federated Learning via Additive and Low-Rank DecompositionCode1
PerAda: Parameter-Efficient Federated Learning Personalization with Generalization GuaranteesCode1
Personalized Federated Continual Learning via Multi-granularity PromptCode1
DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse TrainingCode1
FedAS: Bridging Inconsistency in Personalized Federated LearningCode1
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