IEEE ICASSP 2022

In many emerging machine learning scenarios, intelligent mobile devices, research teams, and startup companies often need to cooperate to gain side information and improve their local learning task, but without sharing sensitive data, models, and objective tasks. This demo will show a novel framework of collaborative learning based on a recently proposed technology named “Assisted Learning” (NeurIPS spotlight presentation). In particular, the demo will provide an online web link to the ICASSP audience, from where a participant can download a Python-based GUI developed and pre-configured by our team. With this GUI, a participant can load local data (pre-installed) and initialize a real-time connection with another anonymous participant. Then, the participant can click a button and start to receive automated assistance from the others by sharing limited statistics, from which reverse engineering of local data, model, and task label is unlikely. A participant is expected to see consistent improvement of prediction performance over time, given that the underlying participants share common interests (such as sharing features or data cases). The novelty of this demo is two-fold. First, the innovation shows that learning organizations may enhance their machine learning performance without leaking proprietary information to collaborators; Second, the demo will show the ICASSP audience how individual data scientists may use the related technology to team up with others quickly, effectively, and anonymously. We envision the demo to bring broad impact to signal processing communities by raising significant interest in areas such as secure machine learning, decentralized signal processing, and collaborative learning algorithms. In addition, the demonstrated techniques will have numerous positive ethical and societal consequences, which match the theme of human-centric signal processing in the ICASSP 2022.