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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

Showing 5160 of 311 papers

TitleStatusHype
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
FLIS: Clustered Federated Learning via Inference Similarity for Non-IID Data DistributionCode1
On Bridging Generic and Personalized Federated Learning for Image ClassificationCode1
PerAda: Parameter-Efficient Federated Learning Personalization with Generalization GuaranteesCode1
Efficient Split-Mix Federated Learning for On-Demand and In-Situ CustomizationCode1
Personalized Federated Learning with First Order Model OptimizationCode1
Unlocking the Potential of Prompt-Tuning in Bridging Generalized and Personalized Federated LearningCode1
BTFL: A Bayesian-based Test-Time Generalization Method for Internal and External Data Distributions in Federated learningCode0
Aggregating Intrinsic Information to Enhance BCI Performance through Federated LearningCode0
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