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. Nowadays, data-driven approaches are employed virtually in every single step of contemporary production processes and business decision makers should take under careful consideration the output of automatized systems that are usually based on machine and deep learning approaches. However, fragmentation of data, in the sense that chunks of data could possibly be distributed in geo-graphically 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. Furthermore, in some industrial sectors, local data privacy heavily enforces rendering the gathering of all data, while training on all data at once is often infeasible. For these reasons, federated learning techniques aim to train better “global” models based on the integration of models which are locally trained with limited datasets, while at the same time preserving the data privacy. Moreover, in regulated industrial sectors, such as in the medical and the pharmaceutical fields, sector-specific legislation imposes strict criteria and rules for the 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. Such a system allows federated training of deep learning models, where initial and intermediate network configurations as well as training data are stored in a blockchain, in order to ensure the transparency and the immutability of the training process and also for minimizing the operational and computational overhead. The applicability of the proposed framework has been valdated by applying three state-of-the-art federated learning algorithms on an industrial pharmaceutical dataset based on two manufacturing lines. The framework validation has focused on the tasks of missing data imputation, outlier detection, and predictive maintenance and the testing results have been promising in terms of both generalizability and convergence time.

Nowadays, data integrity has become a critical issue in the pharmaceutical regulatory landscape, one that requires data to be compliant to ALCOA principles (i.e., data must be Attributable, Legible, Contemporaneous, Original, and Accurate). In this paper, we propose a method which exploits semantic web technologies to represent pharma manufacturing data in a unified manner and evaluate in a systematic manner their ALCOA compliance. To this purpose, in the context of a pharma manufacturing environment, a data integrity ontology (DIOnt) is proposed to be utilized as the basis for the semantic representation of pharma production data and the associated regulatory compliance management processes. We further show that semantic annotations can be used to represent the required ALCOA compliance information, and that semantic reasoning combined with SQWRL queries can be used to evaluate ALCOA compliance. The proposed approach has been implemented in a proof-of-concept prototype and validated with real world pharma manufacturing data, supporting the combined execution of SWRL rules and SQWRL queries with the aim to support the ALCOA compliance assessment and calculate non-compliance percentages per each ALCOA principle.