Enhancing Data Privacy and Security in Corporate Environments via Federated Learning: A Case Study

In an era in which data privacy and security are of the highest priority, organizations are facing with the challenge of simultaneously implementing stringent data protection measures and utilizing a substantial quantity of data for research and development. The paper proposed a novel federated learning framework as a dependable solution that facilitates third-party research without disclosing sensitive corporate data. Federated learning enables the company's local systems to conduct data training and experimentation, thereby ensuring that unprocessed data never leaves a secure environment. In order to assure the data's confidentiality and integrity, the aggregated results are subsequently transmitted to a centralized system for evaluation. By examining case study and experimental results, the viability of federated learning is illustrated in protecting sensitive information and facilitating advanced research. The findings suggest that federated learning not only improves collaborative activities across a diverse range of research domains but also maintains data security.