Assisted Learning: A Framework for Multi-Organization Learning
It addresses the need for privacy-preserving multi-organization learning, which is incremental as it builds on existing federated or collaborative learning concepts.
The paper tackles the problem of enabling secure collaborations among organizations for supervised learning without sharing proprietary algorithms or data, achieving near-oracle performance comparable to centralized training in experiments.
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.