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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 76–100 of 311 papers

TitleStatusHype
Semantic Communication in Dynamic Channel Scenarios: Collaborative Optimization of Dual-Pipeline Joint Source-Channel Coding and Personalized Federated Learning—0
Mitigating Membership Inference Vulnerability in Personalized Federated Learning—0
Differential Privacy Personalized Federated Learning Based on Dynamically Sparsified Client Updates—0
BTFL: A Bayesian-based Test-Time Generalization Method for Internal and External Data Distributions in Federated learningCode0
Personalized Federated Learning via Learning Dynamic Graphs—0
WarmFed: Federated Learning with Warm-Start for Globalization and Personalization Via Personalized Diffusion Models—0
Asynchronous Personalized Federated Learning through Global Memorization—0
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
PFedDST: Personalized Federated Learning with Decentralized Selection Training—0
FedAPA: Server-side Gradient-Based Adaptive Personalized Aggregation for Federated Learning on Heterogeneous Data—0
Advancing Personalized Federated Learning: Integrative Approaches with AI for Enhanced Privacy and Customization—0
SAFL: Structure-Aware Personalized Federated Learning via Client-Specific Clustering and SCSI-Guided Model Pruning—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
Look Back for More: Harnessing Historical Sequential Updates for Personalized Federated Adapter Tuning—0
Uncertainty-Aware Label Refinement on Hypergraphs for Personalized Federated Facial Expression RecognitionCode0
FedCALM: Conflict-aware Layer-wise Mitigation for Selective Aggregation in Deeper Personalized Federated Learning—0
Federated Learning of Dynamic Bayesian Network via Continuous Optimization from Time Series DataCode0
FedAH: Aggregated Head for Personalized Federated LearningCode0
Electrical Load Forecasting in Smart Grid: A Personalized Federated Learning Approach—0
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