Federated Learning for Distributed Threat Detection: Protecting Privacy in Cloud Computing

Cloud computing expansion introduces an important security dilemma regarding the protection of user privacy and system safety measures. The existing traditional security solutions base their work on centralized data processing methods which expose users to security threats along with noncompliance risks. Federated Learning (FL) presents distributed cloud environments with an efficient method to conduct threat detection while protecting user information from exposure. A framework shows how FL enables secure components in cloud security by creating protected IDS with anomaly detection capabilities. FL achieves real-time threat detection by implementing decentralized machine learning methods for maintaining confidential data protection. The system evaluation occurs in popular cloud security databases to show its performance in detecting security threats accurately while reducing false alarm occurrences. Security problems among distributed systems receive analysis in this paper for the traffic created by communication channels and model inconsistency phenomena that emerge in operational conditions while adversaries execute attacks on federated frameworks. Research evidence confirms FL security models simultaneously strengthen cloud threat defenses and support both GDPR alongside HIPAA privacy requirements. The research focuses on enhancing FL models for big cloud systems while developing defensive methods against poisonous attacks.