The Role of Federated Learning in Protecting Market Intelligence Against Data Poisoning

The proliferation of data resulting from these as-a-services and the adoption of 5G technology is overwhelmingly high in fact many companies today are trying to figure out how they can better utilize their data in order to create actionable market intelligence this new revolution allows corporations to address opportunities and deliver results as this inscribes to be the scalable advantage in modern business unfortunately data poisoning attacks can be a huge problem for achieving market intelligence and persistence through the hallmarks of data driven decision making process. With this research attempt we aim to explore and understand how federated learning can be applied as an effective and practical counter mechanism against such attacks in future. In this fiducial blearing model, there is further no direct sharing of datasets so even the likelihood of relevant data being tampered with is appreciably lowered. This type of model not only improves data security but also helps to maintain privacy which in turn makes the model fit for sensitive business scenarios. In this instance, federated learning applied techniques may empower global users to cooperatively train predictive systems that are reliable and acceptable by minimizing the adverse effect of powerfully polluted samples on statistical accuracy weighting. In this regard this paper touches on the advantage of federated learning as it comes in to enhance the security of business intelligence systems as well as the security of data of analytics insights.