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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 101–125 of 311 papers

TitleStatusHype
PeFLL: Personalized Federated Learning by Learning to LearnCode0
pFedMoE: Data-Level Personalization with Mixture of Experts for Model-Heterogeneous Personalized Federated LearningCode0
Motley: Benchmarking Heterogeneity and Personalization in Federated LearningCode0
Provably Personalized and Robust Federated LearningCode0
FedAli: Personalized Federated Learning with Aligned Prototypes through Optimal TransportCode0
FedAH: Aggregated Head for Personalized Federated LearningCode0
Learn What You Need in Personalized Federated LearningCode0
DualFed: Enjoying both Generalization and Personalization in Federated Learning via Hierachical RepresentationsCode0
Loop Improvement: An Efficient Approach for Extracting Shared Features from Heterogeneous Data without Central ServerCode0
Low-Resource Machine Translation through the Lens of Personalized Federated LearningCode0
Fusion of Global and Local Knowledge for Personalized Federated LearningCode0
An Equivalence Between Data Poisoning and Byzantine Gradient AttacksCode0
Memory-Based Optimization Methods for Model-Agnostic Meta-Learning and Personalized Federated LearningCode0
Personalization Improves Privacy-Accuracy Tradeoffs in Federated LearningCode0
Personalized Federated Learning with Server-Side InformationCode0
Exploiting Personalized Invariance for Better Out-of-distribution Generalization in Federated Learning—0
Formal Logic Enabled Personalized Federated Learning Through Property Inference—0
Friends in Unexpected Places: Enhancing Local Fairness in Federated Learning through Clustering—0
Federated Asymptotics: a model to compare federated learning algorithms—0
Find Your Friends: Personalized Federated Learning with the Right Collaborators—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
FedSub: Introducing class-aware Subnetworks Fusion to Enhance Personalized Federated Learning in Ubiquitous Systems—0
Electrical Load Forecasting over Multihop Smart Metering Networks with Federated Learning—0
FedSPD: A Soft-clustering Approach for Personalized Decentralized Federated Learning—0
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