Privacy Preserving Federated Learning with Blockchain for Validating and Enhancing Technology Business Incubation Performance Prediction Models

Engineering notes

In the process, no private and sensitive business data is shared outside thenetwork, significantly reducing the risk of privacy breaches. The simulation results show that our model outperforms the centralizedperformance prediction models in terms of accuracy, precision, recall, and F1-score.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

Technology Business Incubation (TBIs) has become a global phenomenon integral to the growth of regionalinnovation and startup ecosystems. The availability of high-quality infrastructure and facilities lays the foundations of theentire startup ecosystem for providing essential support services that directly impact entrepreneurial success. Theincubation capacity of TBIs across different regions can foster competition and collaboration among these regions,provide avenues for enhancing enterprises’ incubation capabilities, and assist entrepreneurs in assessing the strength ofregional incubation. However, with their rapid expansion, the performance evaluation also becomes increasingly complexdue to the diversity of converging factors such as complex technologies, varying nature of relationships of VCs, andentrepreneurial competencies of the founders incubating startups at the TBIs. Traditional Machine Learning performanceevaluation and prediction models struggle to capture these dynamic variables, while also suffering from privacyvulnerabilities, low accuracy, and reliance on centralized third parties. This often leads to single points of failure,performance bottlenecks, and sometimes increased costs. To address these challenges, we employed Privacy-PreservingFederated Learning with Blockchain (PPFL-BC), a novel framework designed for improving the mechanism ofperformance measurement and prediction for remote TBIs while ensuring that the privacy of entities and the dataremains secure. We utilize capabilities of Artificial Neural Network (ANN) and gradient boosting-enabled federatedlearning to train the model of each TBI locally. In the process, no private and sensitive business data is shared outside thenetwork, significantly reducing the risk of privacy breaches. Besides this, all the locally trained models are aggregatedinto a unified predictive model at the central aggregation unit, which ultimately improves the overall accuracy of theperformance prediction mechanism for the entire population of TBIs. In our model, the decentralized blockchainnetwork is also used to address security concerns related to unauthorized access and data manipulation thereby ensuringtransparent and tamper-proof model updates. We evaluate the performance of our proposed PPFL-BC model by utilizingreal-world business incubation datasets. The simulation results show that our model outperforms the centralizedperformance prediction models in terms of accuracy, precision, recall, and F1-score. The results show that the proposedPPFL-BC model outperforms benchmark models with an accuracy of 84% and precision of 0.92, which shows theefficiency and reliability of our model in predicting and validating TBI success rates.

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