Privacy-Preserving Federated Learning for Collaborative Detection

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This presentation delves into the realm of Privacy-Preserving Federated Learning (FL), addressing centralized detection challenges and the advantages of FL in enhancing data privacy. It explores key areas such as intrusion detection, privacy techniques, and strategies for dealing with Non-IID data within federated settings. The methodology focuses on system design, privacy-adaptive strategies, and aggregation techniques. Experiments evaluate FL models using non-IID data simulations, with...