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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 51–100 of 311 papers

TitleStatusHype
Personalized Federated Learning using HypernetworksCode1
Dual‑detector Re‑optimization for Federated Weakly Supervised Video Anomaly Detection Via Adaptive Dynamic Recursive MappingCode1
Personalized Federated Learning with Moreau EnvelopesCode1
Confidence-aware Personalized Federated Learning via Variational Expectation MaximizationCode1
PFL-MoE: Personalized Federated Learning Based on Mixture of ExpertsCode1
Efficient Split-Mix Federated Learning for On-Demand and In-Situ CustomizationCode1
A Comprehensive View of Personalized Federated Learning on Heterogeneous Clinical DatasetsCode1
FLIS: Clustered Federated Learning via Inference Similarity for Non-IID Data DistributionCode1
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
Federated Asymptotics: a model to compare federated learning algorithms—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
Find Your Friends: Personalized Federated Learning with the Right Collaborators—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
Bad-PFL: Exploring Backdoor Attacks against Personalized Federated Learning—0
FedSub: Introducing class-aware Subnetworks Fusion to Enhance Personalized Federated Learning in Ubiquitous Systems—0
Advancing Personalized Federated Learning: Group Privacy, Fairness, and Beyond—0
FediOS: Decoupling Orthogonal Subspaces for Personalization in Feature-skew Federated Learning—0
Differential Privacy Personalized Federated Learning Based on Dynamically Sparsified Client Updates—0
A Theorem of the Alternative for Personalized Federated Learning—0
Formal Logic Enabled Personalized Federated Learning Through Property Inference—0
FedGlu: A personalized federated learning-based glucose forecasting algorithm for improved performance in glycemic excursion regions—0
Decentralized Personalized Federated Learning for Min-Max Problems—0
FedSelect: Customized Selection of Parameters for Fine-Tuning during Personalized Federated Learning—0
Decentralized Personalized Federated Learning—0
Federated Learning with Unlabeled Clients: Personalization Can Happen in Low Dimensions—0
A Personalized Federated Learning Algorithm: an Application in Anomaly Detection—0
Advances and Challenges in Meta-Learning: A Technical Review—0
FedSheafHN: Personalized Federated Learning on Graph-structured Data—0
Federated Neural Compression Under Heterogeneous Data—0
Federated Learning of Shareable Bases for Personalization-Friendly Image Classification—0
Asynchronous Personalized Federated Learning through Global Memorization—0
Decentralized Directed Collaboration for Personalized Federated Learning—0
FedGradNorm: Personalized Federated Gradient-Normalized Multi-Task Learning—0
Federated Learning for Chronic Obstructive Pulmonary Disease Classification with Partial Personalized Attention Mechanism—0
DA-PFL: Dynamic Affinity Aggregation for Personalized Federated Learning—0
Dirichlet-based Uncertainty Quantification for Personalized Federated Learning with Improved Posterior Networks—0
pFedLoRA: Model-Heterogeneous Personalized Federated Learning with LoRA Tuning—0
A Parameter Aggregation Strategy on Personalized Federated Learning—0
FedPAE: Peer-Adaptive Ensemble Learning for Asynchronous and Model-Heterogeneous Federated Learning—0
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