Balanced coarse-to-fine federated learning for noisy heterogeneous clients

Balanced coarse-to-fine federated learning for noisy heterogeneous clients

Abstract For heterogeneous federated learning, each client cannot ensure the reliability due to the uncertainty in data collection, where different types of noise are always introduced into heterogeneous clients. Current existing methods rely on the specific assumptions for the distribution of noise...

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Bibliographic Details
Main Authors: Longfei Han, Ying Zhai, Yanan Jia, Qiang Cai, Haisheng Li, Xiankai Huang
Format: Article
Language:English
Published: Springer 2025-01-01
Series:Complex & Intelligent Systems
Subjects:
Noisy data
Heterogeneous clients
Self-paced learning
Robust federated learning
Online Access:https://doi.org/10.1007/s40747-024-01694-8
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