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 191–200 of 311 papers

TitleStatusHype
UAV-assisted Online Machine Learning over Multi-Tiered Networks: A Hierarchical Nested Personalized Federated Learning Approach—0
Viewport Prediction, Bitrate Selection, and Beamforming Design for THz-Enabled 360° Video Streaming—0
Visual Prompt Based Personalized Federated Learning—0
WAFFLE: Weighted Averaging for Personalized Federated Learning—0
WarmFed: Federated Learning with Warm-Start for Globalization and Personalization Via Personalized Diffusion Models—0
Which Client is Reliable?: A Reliable and Personalized Prompt-based Federated Learning for Medical Image Question Answering—0
RobWE: Robust Watermark Embedding for Personalized Federated Learning Model Ownership Protection—0
Achieving Personalized Federated Learning with Sparse Local Models—0
A Coalition Formation Game Approach for Personalized Federated Learning—0
ActPerFL: Active Personalized Federated Learning—0
Show:102550
← PrevPage 20 of 32Next →

No leaderboard results yet.