  It is quite popular nowadays for researchers and data analysts holding
different datasets to seek assistance from each other to enhance their modeling
performance. We consider a scenario where different learners hold datasets with
potentially distinct variables, and their observations can be aligned by a
nonprivate identifier. Their collaboration faces the following difficulties:
First, learners may need to keep data values or even variable names undisclosed
due to, e.g., commercial interest or privacy regulations; second, there are
restrictions on the number of transmission rounds between them due to e.g.,
communication costs. To address these challenges, we develop a two-stage
assisted learning architecture for an agent, Alice, to seek assistance from
another agent, Bob. In the first stage, we propose a privacy-aware hypothesis
testing-based screening method for Alice to decide on the usefulness of the
data from Bob, in a way that only requires Bob to transmit sketchy data. Once
Alice recognizes Bob's usefulness, Alice and Bob move to the second stage,
where they jointly apply a synergistic iterative model training procedure. With
limited transmissions of summary statistics, we show that Alice can achieve the
oracle performance as if the training were from centralized data, both
theoretically and numerically.
