VeSAFL: Verifiable Secure Aggregation for Privacy-Preserving Federated Learning

With the proliferation of IoT devices and the exponential growth of data generated at the edge, federated learning (FL) emerges as a powerful method for training machine learning models on decentralized data sources. In security-critical applications, such as anomaly and threat detection, ensuring the confidentiality and integrity of sensitive data is paramount. In this paper, we introduce VeSAFL, a novel scheme for verifiable secure aggregation for privacy-preserving FL designed for edge computing environments. VeSAFL decentralizes model updates across edge nodes, reducing dependency on centralized cloud servers and mitigating risks associated with single points of failure. By leveraging multi-server aggregators, our approach fortifies system resilience and reliability against potential cyber threats. To bolster trust in the learning process, we implement a robust verification mechanism that guarantees the integrity and authenticity of local and global updates. Our experimental results highlight the efficacy and efficiency of VeSAFL in safeguarding against active adversaries and accurately identifying anomalous activities. Furthermore, our comprehensive security analysis affirms the scheme's correctness, verifiability, and privacy preservation in adversarial scenarios.