A Federated Learning Framework for Enforcing Traceability in Manufacturing Processes

Abstract

The plethora of available data in various manufacturing facilities has boosted the adoption of various data analytics methods, which are tailored to a wide range of operations and tasks. However, fragmentation of data, in the sense that chunks of data could possibly be distributed in geographically sparse areas, hampers the generation of better and more accurate intelligent models that would otherwise benefit from the larger quantities of available data which are derived from various operations taking place at different locations of a manufacturing process. Moreover, in regulated industrial sectors, such as in the medical and the pharmaceutical fields, sector-specific legislation imposes strict criteria and rules for the privacy, maintenance and long-term storage of data. Process reproducibility is often an essential requirement in these regulated industrial sectors, and this issue could be supported by AI models which can be applied to enforce traceability, auditability and integrity of every initial, intermediate and final piece of data used during the AI model training process. In this respect, blockchain technologies could be potentially also useful for enabling and enforcing such requirements. In this paper, we present a multi-blockchain-based platform integrated with federated learning functionalities to train global AI (deep learning) models. The proposed platform maintains an audit trail of all information pertaining the training process using a set of blockchains in order to ensure the training process’s immutability. The applicability of the proposed framework has been validated on three tasks by applying three state-of-the-art federated learning algorithms on an industrial pharmaceutical dataset based on two manufacturing lines, achieving promising in terms of both generalizability and convergence time.