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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 61–70 of 311 papers

TitleStatusHype
Convergence-Privacy-Fairness Trade-Off in Personalized Federated Learning—0
pFedSOP : Accelerating Training Of Personalized Federated Learning Using Second-Order Optimization—0
Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling—0
Generalized and Personalized Federated Learning with Foundation Models via Orthogonal Transformations—0
Federated Learning with Unlabeled Clients: Personalization Can Happen in Low Dimensions—0
Personalized Federated Learning under Model Dissimilarity Constraints—0
Incentivizing Inclusive Contributions in Model Sharing Markets—0
Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data—0
Privacy-Preserving Personalized Federated Learning for Distributed Photovoltaic Disaggregation under Statistical Heterogeneity—0
DP2FL: Dual Prompt Personalized Federated Learning in Foundation Models—0
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