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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 26–50 of 311 papers

TitleStatusHype
Personalized Federated Learning for Egocentric Video Gaze Estimation with Comprehensive Parameter Frezzing—0
Electrical Load Forecasting over Multihop Smart Metering Networks with Federated Learning—0
FedAPA: Server-side Gradient-Based Adaptive Personalized Aggregation for Federated Learning on Heterogeneous Data—0
PFedDST: Personalized Federated Learning with Decentralized Selection Training—0
PM-MOE: Mixture of Experts on Private Model Parameters for Personalized Federated LearningCode1
SAFL: Structure-Aware Personalized Federated Learning via Client-Specific Clustering and SCSI-Guided Model Pruning—0
Advancing Personalized Federated Learning: Integrative Approaches with AI for Enhanced Privacy and Customization—0
Heterogeneity-aware Personalized Federated Learning via Adaptive Dual-Agent Reinforcement Learning—0
Integrating Personalized Federated Learning with Control Systems for Enhanced Performance—0
Bad-PFL: Exploring Backdoor Attacks against Personalized Federated Learning—0
Personalized Federated Learning for Cellular VR: Online Learning and Dynamic Caching—0
pMixFed: Efficient Personalized Federated Learning through Adaptive Layer-Wise Mixup—0
pFedWN: A Personalized Federated Learning Framework for D2D Wireless Networks with Heterogeneous Data—0
Uncertainty-Aware Label Refinement on Hypergraphs for Personalized Federated Facial Expression RecognitionCode0
Look Back for More: Harnessing Historical Sequential Updates for Personalized Federated Adapter Tuning—0
FedCALM: Conflict-aware Layer-wise Mitigation for Selective Aggregation in Deeper Personalized Federated Learning—0
Calibre: Towards Fair and Accurate Personalized Federated Learning with Self-Supervised LearningCode3
Federated Learning of Dynamic Bayesian Network via Continuous Optimization from Time Series DataCode0
Optimizing Personalized Federated Learning through Adaptive Layer-Wise LearningCode1
FedAH: Aggregated Head for Personalized Federated LearningCode0
FedAli: Personalized Federated Learning with Aligned Prototypes through Optimal TransportCode0
Electrical Load Forecasting in Smart Grid: A Personalized Federated Learning Approach—0
FedSub: Introducing class-aware Subnetworks Fusion to Enhance Personalized Federated Learning in Ubiquitous Systems—0
Personalized Federated Learning for Cross-view Geo-localization—0
Towards Personalized Federated Learning via Comprehensive Knowledge Distillation—0
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