SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 211–220 of 311 papers

TitleStatusHype
Federated Neural Compression Under Heterogeneous Data—0
pFedSim: Similarity-Aware Model Aggregation Towards Personalized Federated Learning—0
Towards More Suitable Personalization in Federated Learning via Decentralized Partial Model Training—0
A novel parameter decoupling approach of personalized federated learning for image analysis—0
FedDWA: Personalized Federated Learning with Dynamic Weight AdjustmentCode0
Collaborative Chinese Text Recognition with Personalized Federated Learning—0
Mobilizing Personalized Federated Learning in Infrastructure-Less and Heterogeneous Environments via Random Walk Stochastic ADMM—0
Personalized Federated Learning via Gradient Modulation for Heterogeneous Text Summarization—0
Federated Learning of Shareable Bases for Personalization-Friendly Image Classification—0
IP-FL: Incentivized and Personalized Federated Learning—0
Show:102550
← PrevPage 22 of 32Next →

No leaderboard results yet.