A Federated and Privacy-Preserving Architecture for Scalable Collaborative Spam Detection in Distributed Multi-Cloud Environments

The increasing prevalence of spam traffic poses major challenges for distributed and multi-cloud computing environments, particularly regarding scalability, workload coordination, and data privacy. Modern cluster-based infrastructures require secure and efficient mechanisms to enable collaborative spam detection across heterogeneous providers while preserving local autonomy. This paper introduces FMH-SCS, a federated and privacy-preserving distributed architecture that enables collaborative spam detection in multi-cloud cluster environments. FMH-SCS integrates Federated Learning with Secure Multi-Party Computation and Homomorphic Encryption, ensuring that sensitive email data remains local while only encrypted model updates are exchanged across computing clusters. To address communication bottlenecks in large-scale distributed training, FMH-SCS employs optimized aggregation protocols that balance security with communication efficiency. We evaluate FMH-SCS using the Enron and SpamAssassin benchmark datasets, analyzing accuracy, precision, recall, F1-score, training overhead, and communication costs in distributed setups. Experimental results show that FMH-SCS improves spam detection accuracy by up to 6.12% compared with state-of-the-art baselines, while also reducing communication and synchronization overhead across distributed clusters. These findings demonstrate that FMH-SCS provides a scalable, privacy-preserving, and computation-efficient solution for collaborative spam detection in modern cluster and distributed computing environments.