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News/FedSIR: Spectral Client Identification and Relabeling for Federated Learning with Noisy Labels
arxiv
PublishedApril 24, 2026 at 4:00 AM
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FedSIR: Spectral Client Identification and Relabeling for Federated Learning with Noisy Labels

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arxiv.orgfull article ↗
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Publisher summary· verbatim

arXiv:2604.20825v1 Announce Type: new Abstract: Federated learning (FL) enables collaborative model training without sharing raw data; however, the presence of noisy labels across distributed clients can severely degrade the learning performance. In this paper, we propose FedSIR, a multi-stage frame

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Discussion
Mentioned models
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  • 01
    FedSIR
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arxiv
Read original ↗All from arxiv →
Tags
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#federated-learning#noisy-labels#robust-training#computer-vision

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Mentioned models
01
  • 01
    FedSIR
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#federated-learning#noisy-labels#robust-training#computer-vision

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