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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 51–75 of 311 papers

TitleStatusHype
Federated Multi-Task Learning under a Mixture of DistributionsCode1
Dual‑detector Re‑optimization for Federated Weakly Supervised Video Anomaly Detection Via Adaptive Dynamic Recursive MappingCode1
FedAS: Bridging Inconsistency in Personalized Federated LearningCode1
Adaptive Test-Time Personalization for Federated LearningCode1
FedSelect: Personalized Federated Learning with Customized Selection of Parameters for Fine-TuningCode1
Unlocking the Potential of Prompt-Tuning in Bridging Generalized and Personalized Federated LearningCode1
Personalized Federated Continual Learning via Multi-granularity PromptCode1
FedSSA: Semantic Similarity-based Aggregation for Efficient Model-Heterogeneous Personalized Federated LearningCode1
Exploiting Personalized Invariance for Better Out-of-distribution Generalization in Federated Learning—0
Friends in Unexpected Places: Enhancing Local Fairness in Federated Learning through Clustering—0
Agnostic Personalized Federated Learning with Kernel Factorization—0
Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling—0
Energy-Aware Edge Association for Cluster-based Personalized Federated Learning—0
Electrical Load Forecasting over Multihop Smart Metering Networks with Federated Learning—0
Electrical Load Forecasting in Smart Grid: A Personalized Federated Learning Approach—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
Federated Learning with Unlabeled Clients: Personalization Can Happen in Low Dimensions—0
Efficient Cluster Selection for Personalized Federated Learning: A Multi-Armed Bandit Approach—0
Bayesian Personalized Federated Learning with Shared and Personalized Uncertainty Representations—0
Advancing Personalized Federated Learning: Integrative Approaches with AI for Enhanced Privacy and Customization—0
DP2FL: Dual Prompt Personalized Federated Learning in Foundation Models—0
Bayesian Neural Network For Personalized Federated Learning Parameter Selection—0
Achieving Personalized Federated Learning with Sparse Local Models—0
DP^2-FedSAM: Enhancing Differentially Private Federated Learning Through Personalized Sharpness-Aware Minimization—0
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