FedSeq: Personalized Federated Learning via Sequential Layer Expansion in Representation Learning

FedSeq: Personalized Federated Learning via Sequential Layer Expansion in Representation Learning

Federated learning ensures the privacy of clients by conducting distributed training on individual client devices and sharing only the model weights with a central server. However, in real-world scenarios, especially in IoT scenarios where devices have varying capabilities and data heterogeneity exi...

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Bibliographic Details
Main Authors: Jae Won Jang, Bong Jun Choi
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:Applied Sciences
Subjects:
federated learning (FL)
data heterogeneity
personalized federated learning (PFL)
representation learning
meta-learning
Online Access:https://www.mdpi.com/2076-3417/14/24/12024

Internet

https://www.mdpi.com/2076-3417/14/24/12024

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