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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 271–280 of 311 papers

TitleStatusHype
An Equivalence Between Data Poisoning and Byzantine Gradient AttacksCode0
Personalization Improves Privacy-Accuracy Tradeoffs in Federated LearningCode0
Energy-Aware Edge Association for Cluster-based Personalized Federated Learning—0
A Coalition Formation Game Approach for Personalized Federated Learning—0
Personalized Federated Learning via Convex Clustering—0
Factorized-FL: Agnostic Personalized Federated Learning with Kernel Factorization & Similarity Matching—0
Achieving Personalized Federated Learning with Sparse Local Models—0
How to Backdoor HyperNetwork in Personalized Federated Learning?—0
Personalized Federated Learning of Driver Prediction Models for Autonomous Driving—0
PartialFed: Cross-Domain Personalized Federated Learning via Partial Initialization—0
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