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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 21–30 of 311 papers

TitleStatusHype
Mitigating Membership Inference Vulnerability in Personalized Federated Learning—0
BTFL: A Bayesian-based Test-Time Generalization Method for Internal and External Data Distributions in Federated learningCode0
Personalized Federated Learning via Learning Dynamic Graphs—0
WarmFed: Federated Learning with Warm-Start for Globalization and Personalization Via Personalized Diffusion Models—0
Asynchronous Personalized Federated Learning through Global Memorization—0
Personalized Federated Learning for Egocentric Video Gaze Estimation with Comprehensive Parameter Frezzing—0
Electrical Load Forecasting over Multihop Smart Metering Networks with Federated Learning—0
PFedDST: Personalized Federated Learning with Decentralized Selection Training—0
FedAPA: Server-side Gradient-Based Adaptive Personalized Aggregation for Federated Learning on Heterogeneous Data—0
PM-MOE: Mixture of Experts on Private Model Parameters for Personalized Federated LearningCode1
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