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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 51–60 of 311 papers

TitleStatusHype
Exploiting Shared Representations for Personalized Federated LearningCode1
A New Look and Convergence Rate of Federated Multi-Task Learning with Laplacian RegularizationCode1
PFL-MoE: Personalized Federated Learning Based on Mixture of ExpertsCode1
Adaptive Intrusion Detection in the Networking of Large-Scale LANs with Segmented Federated LearningCode1
Personalized Federated Learning with First Order Model OptimizationCode1
Ditto: Fair and Robust Federated Learning Through PersonalizationCode1
Intrusion Detection with Segmented Federated Learning for Large-Scale Multiple LANsCode1
Personalized Federated Learning with Moreau EnvelopesCode1
pFedMMA: Personalized Federated Fine-Tuning with Multi-Modal Adapter for Vision-Language ModelsCode0
Personalized Federated Learning via Dual-Prompt Optimization and Cross Fusion—0
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