Macro-level Inference in Collaborative Learning

Abstract

With increasing data collection, also efforts to extract the underlying knowledge increase. Among these, collaborative learning efforts become more important, where multiple organisations want to jointly learn a common predictive model, e.g. to detect anomalies or learn how to improve a production process. Instead of learning only from their own data, a collaborative approach enables the participants to learn a more generalising model, also capable to predict settings not yet encountered by their own organisation, but some of the others. However, in many cases, the participants would not want to directly share and disclose their data, for regulatory reasons, or because the data constitute a business asset. Approaches such as federated learning allow to train a collaborative model without exposing the data itself. However, federated learning still requires exchanging intermediate models from each participant. Information that can be inferred from these models is thus a concern. Threats to individual data points and defences have been studied e.g. in membership inference attacks. However, we argue that in many use cases, also global properties are of interest -- not only to outsiders, but specifically also to the other participants, which might be competitors. In a production process, e.g. knowing which types of steps a company performs frequently, or obtaining information on quantities of a specific product or material a company processes, could reveal business secrets, without needing to know details of individual data points.