Coupling Neural Networks Between Clusters for Better Personalized Care - Kraus, Mathias; Hambauer, Nico; Müller, Kristina; Kröckel, Pavlina; Ulapane, Nalika; De Caigny, Arno; De Bock, Koen; Coussement, Kristof (Proceedings of the 57th Hawaii International Conference on System Sciences, 2024)
Personalized healthcare powered by machine learning (ML) is at the forefront of modern medicine, promising to optimize treatment outcomes, reduce adverse effects, and improve patient satisfaction. However, simple ML models generally lack the complexity to accurately model individual characteristics, while powerful ML models require large amounts of data, which are often unavailable in the healthcare domain. We address this problem with cluster-level personalization. In this method, similar patients are grouped into clusters and a local ML model is trained for each cluster. Since the amount of patient data to train ML models naturally decreases for each cluster, we introduce a novel objective function called "coupling" that allows information to be shared between clusters, so that smaller clusters can also benefit from information from larger clusters, thereby improving patient outcome prediction. Our method provides a compromise between a single global model for all patients and completely independent local cluster models. We show that coupling leads to statistically significant improvements on a simulated and a real-world dataset in the context of diabetes.<br
Keywords: Personal Health Management with Digital Solutions; machine learning; neural networks; optimization; personalized care

Cross-silo federated learning in enterprise networks with cooperative and competing actors - Müller, Kristina; Bodendorf, Freimut (The Human Side of Service Engineering. AHFE (2023) International Conference., 2023)
The performance and generalizability of AI-based enterprise applications depends on the quantity, quality, and diversity of training data. However, data usually exi- sts in the form of data silos at individual sites. With cross-silo federated learning knowledge extracted from data silos that are distributed across multiple enterpri- ses can be combined to improve the predictive performance of AI models without sharing and centralizing potentially sensitive raw data. The decentralized learning approach thus offers new privacy-preserving opportunities for cross-company colla- boration, knowledge management, and the development of intelligent applications and services in federated enterprise networks. Since federated learning enables collaboration between both cooperating and competing companies, this literature review of application-based papers analyzes the differences in the design and stra- tegic management of federated enterprise networks as a function of the actors’ relationships.
Keywords: Cross-silo federated learning; Federated networks; Enterprise collaboration

Ethical AI Principles for Enterprise Collaboration in Federated Learning Networks - Müller, Kristina; Kolb, Lisa; Lechner, Ulrike; Bodendorf, Freimut (European Conference on Information Systems, 2024)
Ethical artificial intelligence (AI) has received increasing attention as the development and use of AI applications have expanded. Collaborative machine learning, such as cross-silo federated learning (FL) can assist in adhering to ethical AI standards. FL ensures privacy and data sovereignty while minimizing model bias by aggregating AI models trained locally on data silos from several organizations. However, decentralization and multi-party involvement necessitate the refinement of existing ethical AI principles and the development or adaptation of methods for compliance. In this study, we conduct a systematic literature review followed by a workshop with participants from academia and industry on ethical principles for FL, considering technical and organizational aspects at different phases of the collaboration cycle. Our contribution is a guideline for technical and non-technical stakeholders to support the ethically aligned establishment/entry, value co-creation, operational continuity, and exit/termination of enterprise FL networks.<br

Sustainable CRISP-DM Extension for Energy-Aware AI Development - Müller, Kristina; Kröckel, Pavlina; Bodendorf, Freimut (AMCIS 2023 Proceedings, 2023)
AI-based solutions show great potential in various fields, including the context of sustainability. In light of the great potential, it is often overlooked that advances in performance come at a significant cost to the environment, as training data- and computation-intensive models involves high carbon emissions. Climate change and its increasing awareness are forcing companies to use available resources more efficiently, which for the field of AI means developing accurate models in an energy-aware manner. We conduct a systematic literature review on approaches for sustainable AI development and organize the existing knowledge along the phases of the established CRISP-DM model. In this way, we provide managers and developers with a holistic picture of opportunities for reducing the environmental footprint in all phases of typical enterprise AI projects.<br