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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
Tensor Decomposition based Personalized Federated Learning—0
FedMCSA: Personalized Federated Learning via Model Components Self-Attention—0
FLIS: Clustered Federated Learning via Inference Similarity for Non-IID Data DistributionCode1
Personalizing or Not: Dynamically Personalized Federated Learning with Incentives—0
Mitigating Biases in Student Performance Prediction via Attention-Based Personalized Federated Learning—0
One Model to Unite Them All: Personalized Federated Learning of Multi-Contrast MRI SynthesisCode1
An Empirical Study of Personalized Federated LearningCode1
Motley: Benchmarking Heterogeneity and Personalization in Federated LearningCode0
Personalized Federated Learning via Variational Bayesian InferenceCode1
Adaptive Expert Models for Personalization in Federated LearningCode0
Hypernetwork-based Personalized Federated Learning for Multi-Institutional CT ImagingCode1
pFL-Bench: A Comprehensive Benchmark for Personalized Federated Learning—0
Group privacy for personalized federated learning—0
An Optimal Transport Approach to Personalized Federated Learning—0
Straggler-Resilient Personalized Federated LearningCode0
DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse TrainingCode1
Personalized Federated Learning with Server-Side InformationCode0
ActPerFL: Active Personalized Federated Learning—0
Personalized Federated Learning with Multiple Known ClustersCode0
Self-Aware Personalized Federated Learning—0
CDKT-FL: Cross-Device Knowledge Transfer using Proxy Dataset in Federated Learning—0
FedGradNorm: Personalized Federated Gradient-Normalized Multi-Task Learning—0
Efficient Split-Mix Federated Learning for On-Demand and In-Situ CustomizationCode1
SemiPFL: Personalized Semi-Supervised Federated Learning Framework for Edge IntelligenceCode1
Personalized Federated Learning With GraphCode1
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