  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 confidential. The prognostic model first
employs multivariate functional principal component analysis to fuse the
multi-stream degradation signals. Then, the fused features coupled with the
times-to-failure are utilized to build a (log)-location-scale regression model
for failure prediction. To estimate parameters using distributed datasets and
keep the data privacy of all participants, we propose a new federated algorithm
for feature extraction. Numerical studies indicate that the performance of the
proposed model is the same as that of classic non-federated prognostic models
and is better than that of the models constructed by each user itself.
