FedPS: Federated Preprocessing for structured data via aggregated Statistics
Quick summary
arXiv:2602.10870v2 Announce Type: replace-cross Abstract: Federated Learning (FL) enables multiple parties to collaboratively train machine learning models without sharing raw data. However, before training, data must be preprocessed to address missing values, inconsistent formats, and heterogeneous feature scales. This preprocessing stage is critical for model performance but is largely overlooked in FL research. In practical FL systems, privacy constraints prohibit centralizing raw data, while communication efficiency introduces further challenges for distributed preprocessing. We introduce
Key takeaways
- arXiv:2602.10870v2 Announce Type: replace-cross Abstract: Federated Learning (FL) enables multiple parties to collaboratively train machine learning models without sharing raw data.
- However, before training, data must be preprocessed to address missing values, inconsistent formats, and heterogeneous feature scales.
- This preprocessing stage is critical for model performance but is largely overlooked in FL research.
Why it matters
“FedPS: Federated Preprocessing for structured data via aggregated Statistics” may affect what data AI products can use and where accountability sits. Product teams should watch compliance duties, rights holders should watch enforcement, and users should watch transparency and appeal mechanisms.
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