A Federated Data Fusion-Based Prognostic Model for Applications with Multi-Stream Incomplete Signals

arXiv:2311.07474v2 Announce Type: replace-cross
Abstract: Most prognostic methods require a decent amount of data for model training. In reality, however, the amount of historical data owned by a single organization might be small or not large enough to train a reliable prognostic model. To address this challenge, this article proposes a federated prognostic model that allows multiple users to jointly construct a failure time prediction model using their multi-stream, high-dimensional, and incomplete data while keeping each user's data local and …

abstract applications arxiv challenge cs.lg data eess.sp fusion historical data however organization reality small stat.me stat.ml train training type