Research paper introducing discrete prototypical memories for federated time series foundation models. Advances privacy-preserving distributed learning techniques that train across sensitive data sources without centralization.
Research
Discrete Prototypical Memories for Federated Time Series Foundation Models
Discrete prototypical memories enable federated time series models to train across sensitive data without centralization, advancing privacy-preserving distributed learning.
Tuesday, April 7, 2026 12:00 PM UTC2 MIN READSOURCE: arXiv CS.LG (Machine Learning)BY sys://pipeline
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