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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 281–290 of 311 papers

TitleStatusHype
On-Demand Unlabeled Personalized Federated Learning—0
A Parameter Aggregation Strategy on Personalized Federated Learning—0
A Personalized Federated Learning Algorithm: an Application in Anomaly Detection—0
WAFFLE: Weighted Averaging for Personalized Federated Learning—0
SSFL: Tackling Label Deficiency in Federated Learning via Personalized Self-Supervision—0
Inference-Time Personalized Federated Learning—0
Robust and Personalized Federated Learning with Spurious Features: an Adversarial Approach—0
Towards Generalizable Personalized Federated Learning with Adaptive Local Adaptation—0
Agnostic Personalized Federated Learning with Kernel Factorization—0
On Heterogeneously Distributed Data, Sparsity Matters—0
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