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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 276–300 of 311 papers

TitleStatusHype
Personalized Federated Learning for Heterogeneous Clients with Clustered Knowledge Transfer—0
Connecting Low-Loss Subspace for Personalized Federated LearningCode1
Private Multi-Task Learning: Formulation and Applications to Federated LearningCode0
Federated Multi-Task Learning under a Mixture of DistributionsCode1
Federated Asymptotics: a model to compare federated learning algorithms—0
Personalized Federated Learning with Clustering: Non-IID Heart Rate Variability Data Application—0
New Metrics to Evaluate the Performance and Fairness of Personalized Federated Learning—0
Sparse Personalized Federated LearningCode0
Personalized Federated Learning over non-IID Data for Indoor Localization—0
On Bridging Generic and Personalized Federated Learning for Image ClassificationCode1
UAV-assisted Online Machine Learning over Multi-Tiered Networks: A Hierarchical Nested Personalized Federated Learning Approach—0
Personalized Federated Learning with Gaussian ProcessesCode1
Personalized Federated Learning with Contextualized Generalization—0
Decentralized Personalized Federated Learning for Min-Max Problems—0
Memory-Based Optimization Methods for Model-Agnostic Meta-Learning and Personalized Federated LearningCode0
Personalized Federated Learning by Structured and Unstructured Pruning under Data HeterogeneityCode1
Personalized Federated Learning using HypernetworksCode1
A Theorem of the Alternative for Personalized Federated Learning—0
Towards Personalized Federated Learning—0
Personalized Federated Learning: A Unified Framework and Universal Optimization TechniquesCode0
Exploiting Shared Representations for Personalized Federated LearningCode1
A New Look and Convergence Rate of Federated Multi-Task Learning with Laplacian RegularizationCode1
On Data Efficiency of Meta-learning—0
PFL-MoE: Personalized Federated Learning Based on Mixture of ExpertsCode1
Adaptive Intrusion Detection in the Networking of Large-Scale LANs with Segmented Federated LearningCode1
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