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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 291–300 of 311 papers

Title	Date	Tasks	Status	Hype
Personalized Federated Learning by Structured and Unstructured Pruning under Data Heterogeneity	May 2, 2021	Federated LearningPersonalized Federated Learning	CodeCode Available	1
Personalized Federated Learning using Hypernetworks	Mar 8, 2021	Federated LearningPersonalized Federated Learning	CodeCode Available	1
A Theorem of the Alternative for Personalized Federated Learning	Mar 2, 2021	Federated LearningPersonalized Federated Learning	—Unverified	0
Towards Personalized Federated Learning	Mar 1, 2021	BenchmarkingFederated Learning	—Unverified	0
Personalized Federated Learning: A Unified Framework and Universal Optimization Techniques	Feb 19, 2021	Federated LearningPersonalized Federated Learning	CodeCode Available	0
Exploiting Shared Representations for Personalized Federated Learning	Feb 14, 2021	Federated LearningMeta-Learning	CodeCode Available	1
A New Look and Convergence Rate of Federated Multi-Task Learning with Laplacian Regularization	Feb 14, 2021	Federated LearningFew-Shot Learning	CodeCode Available	1
On Data Efficiency of Meta-learning	Jan 30, 2021	Federated LearningMeta-Learning	—Unverified	0
PFL-MoE: Personalized Federated Learning Based on Mixture of Experts	Dec 31, 2020	Decision MakingFederated Learning	CodeCode Available	1
Adaptive Intrusion Detection in the Networking of Large-Scale LANs with Segmented Federated Learning	Dec 16, 2020	Federated LearningIntrusion Detection	CodeCode Available	1

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