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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 191–200 of 311 papers

TitleStatusHype
Towards Personalized Federated Learning via Comprehensive Knowledge Distillation—0
Towards Personalized Federated Multi-Scenario Multi-Task Recommendation—0
TPFL: A Trustworthy Personalized Federated Learning Framework via Subjective Logic—0
Trustworthy Personalized Bayesian Federated Learning via Posterior Fine-Tune—0
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
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