FedLearnSP: Preserving Privacy and Security Using Federated Learning and Edge Computing

Enormous amount of information is processed at different websites, on a number of different AI tools and in multiple data silos. Sharing data between various sources, this is a significant obstacle, due to administrative, organizational, and security considerations. One possible solution is federated machine learning (FML), a system that simultaneously sends machine learning algorithms to all data sources, trains models at each source, and aggregates the learned models. This technique ensures consumer-influenced solution by processing the data locally. This work is the first to investigate the applicability of Internet attack detection through FML, to the best of our knowledge. Our primary contributions include the application of federated learning to satisfy customer search queries by detecting malicious spam images, which may lead these AI systems to retrieve irrelevant information. We assess and analyze the FML-entangled learning output comprehensively in different ways adjustments including balanced and imbalanced customer data distribution, scalability, and overhead communication. Our measuring results show that FML suits practical scenarios, where the variable image size, including the animation ratio to legitimate samples of images, present among the advertisements that may distract consumer from fetching relevant results. With the evaluated results, the state-of-the-art FedLearnSP proved significant image spam detection.