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Enhancing Privacy and Communication Efficiency in Federated Learning Through Selective Low-Rank Adaptation and Differential Privacy

Federated learning (FL) enables collaborative model training without centralizing raw data, but its application to large-scale vision models remains constrained by high communication cost, data heterogeneity, and privacy risks. Furthermore, in real-world applications such as autonomous driving and h...

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Main Authors: Takuto Miyata, Liuyi Yang, Zhiyi Zhu, Patrick Finnerty, Chikara Ohta
Format: Artigo
Language:Inglês
Published: MDPI AG 2025-12-01
Series:Applied Sciences
Subjects:
BiT
communication efficiency
differential privacy
federated learning
low-rank adaptation
LoRA
Online Access:https://www.mdpi.com/2076-3417/15/24/13102
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