  In an increasing number of AI scenarios, collaborations among different
organizations or agents (e.g., human and robots, mobile units) are often
essential to accomplish an organization-specific mission. However, to avoid
leaking useful and possibly proprietary information, organizations typically
enforce stringent security constraints on sharing modeling algorithms and data,
which significantly limits collaborations. In this work, we introduce the
Assisted Learning framework for organizations to assist each other in
supervised learning tasks without revealing any organization's algorithm, data,
or even task. An organization seeks assistance by broadcasting task-specific
but nonsensitive statistics and incorporating others' feedback in one or more
iterations to eventually improve its predictive performance. Theoretical and
experimental studies, including real-world medical benchmarks, show that
Assisted Learning can often achieve near-oracle learning performance as if data
and training processes were centralized.
