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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 251–275 of 311 papers

TitleStatusHype
Blinder: End-to-end Privacy Protection in Sensing Systems via Personalized Federated LearningCode0
Personalized Federated Learning with Communication Compression—0
Tensor Decomposition based Personalized Federated Learning—0
FedMCSA: Personalized Federated Learning via Model Components Self-Attention—0
Personalizing or Not: Dynamically Personalized Federated Learning with Incentives—0
Mitigating Biases in Student Performance Prediction via Attention-Based Personalized Federated Learning—0
Motley: Benchmarking Heterogeneity and Personalization in Federated LearningCode0
Adaptive Expert Models for Personalization in Federated LearningCode0
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
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
Personalized Federated Learning with Exact Stochastic Gradient Descent—0
PerFED-GAN: Personalized Federated Learning via Generative Adversarial Networks—0
An Equivalence Between Data Poisoning and Byzantine Gradient AttacksCode0
Personalization Improves Privacy-Accuracy Tradeoffs in Federated LearningCode0
Energy-Aware Edge Association for Cluster-based Personalized Federated Learning—0
A Coalition Formation Game Approach for Personalized Federated Learning—0
Personalized Federated Learning via Convex Clustering—0
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