  Tensor factorization has been proved as an efficient unsupervised learning
approach for health data analysis, especially for computational phenotyping,
where the high-dimensional Electronic Health Records (EHRs) with patients'
history of medical procedures, medications, diagnosis, lab tests, etc., are
converted to meaningful and interpretable medical concepts. Federated tensor
factorization distributes the tensor computation to multiple workers under the
coordination of a central server, which enables jointly learning the phenotypes
across multiple hospitals while preserving the privacy of the patient
information. However, existing federated tensor factorization algorithms
encounter the single-point-failure issue with the involvement of the central
server, which is not only easily exposed to external attacks but also limits
the number of clients sharing information with the server under restricted
uplink bandwidth. In this paper, we propose CiderTF, a communication-efficient
decentralized generalized tensor factorization, which reduces the uplink
communication cost by leveraging a four-level communication reduction strategy
designed for a generalized tensor factorization, which has the flexibility of
modeling different tensor distribution with multiple kinds of loss functions.
Experiments on two real-world EHR datasets demonstrate that CiderTF achieves
comparable convergence with a communication reduction up to 99.99%.
