FedRN: Exploiting k-Reliable Neighbors Towards Robust Federated Learning

Robustness is becoming another important challenge of federated learning in that the data collection process in each client is naturally accompanied by noisy labels. However, it is far more complex and challenging owing to varying levels of data heterogeneity and noise over clients, which exacerbate...

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Veröffentlicht in:	arXiv.org 2022-09
Hauptverfasser:	Kim, SangMook, Shin, Wonyoung, Jang, Soohyuk, Song, Hwanjun, Se-Young, Yun
Format:	Artikel
Sprache:	eng
Schlagworte:	Clients Computer Science - Learning Data collection Federated learning Heterogeneity Labels Robustness
Online-Zugang:	Volltext
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