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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 226–250 of 311 papers

TitleStatusHype
Decentralized Personalized Federated Learning for Min-Max Problems—0
Differential Privacy Personalized Federated Learning Based on Dynamically Sparsified Client Updates—0
Dirichlet-based Uncertainty Quantification for Personalized Federated Learning with Improved Posterior Networks—0
DP^2-FedSAM: Enhancing Differentially Private Federated Learning Through Personalized Sharpness-Aware Minimization—0
DP2FL: Dual Prompt Personalized Federated Learning in Foundation Models—0
Efficient Cluster Selection for Personalized Federated Learning: A Multi-Armed Bandit Approach—0
Electrical Load Forecasting in Smart Grid: A Personalized Federated Learning Approach—0
Electrical Load Forecasting over Multihop Smart Metering Networks with Federated Learning—0
Energy-Aware Edge Association for Cluster-based Personalized Federated Learning—0
Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling—0
Friends in Unexpected Places: Enhancing Local Fairness in Federated Learning through Clustering—0
Exploiting Personalized Invariance for Better Out-of-distribution Generalization in Federated Learning—0
Exploring Personalized Federated Learning Architectures for Violence Detection in Surveillance Videos—0
Factor-Assisted Federated Learning for Personalized Optimization with Heterogeneous Data—0
Factorized-FL: Agnostic Personalized Federated Learning with Kernel Factorization & Similarity Matching—0
Fault Detection in Telecom Networks using Bi-level Federated Graph Neural Networks—0
FedABC: Targeting Fair Competition in Personalized Federated Learning—0
Robustness and Personalization in Federated Learning: A Unified Approach via Regularization—0
FedAPA: Server-side Gradient-Based Adaptive Personalized Aggregation for Federated Learning on Heterogeneous Data—0
FedASTA: Federated adaptive spatial-temporal attention for traffic flow prediction—0
FedCALM: Conflict-aware Layer-wise Mitigation for Selective Aggregation in Deeper Personalized Federated Learning—0
FedCRL: Personalized Federated Learning with Contrastive Shared Representations for Label Heterogeneity in Non-IID Data—0
FedD2S: Personalized Data-Free Federated Knowledge Distillation—0
Federated Learning for Chronic Obstructive Pulmonary Disease Classification with Partial Personalized Attention Mechanism—0
Federated Learning of Shareable Bases for Personalization-Friendly Image Classification—0
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