Deploy-able Privacy Preserving Collaborative ML

In the data-driven world, emerging technologies like the Internet of Things (IoT) and other crowd-sourced data sources like mobile devices etc. generate a tremendous volume of decentralized data that needs to be analyzed for obtaining useful insights, necessary for reliable decision making. Although the overall data is rich, contributors of such kind of data are reluctant to share their own data due to serious concerns regarding protection of their privacy; while those interested in harvesting the data are constrained by the limited computational resources available with each participant. In this paper, we propose an end-to-end algorithm that puts in coalescence the mechanism of learning collaboratively in a decentralized fashion, using Federated Learning, while preserving differential privacy of each participating client, which are typically conceived as resource-constrained edge devices. We have developed the proposed infrastructure and analyzed its performance from the standpoint of a machine learning task using standard metrics. We observed that the collaborative learning framework actually increases prediction capabilities in comparison to a centrally trained model (by 1-2%), without having to share data amongst the participants, while strong guarantees on privacy (epsilon, delta) can be provided with some compromise on performance (about 2-4%). Additionally, quantization of the model for deployment on edge devices do not degrade its capability, whilst enhancing the overall system efficiency.