Federated Learning (FL) allows multiple clients to collaboratively train a machine learning model without directly sharing their raw data. While federated learning provides a degree of data privacy, model updates are still susceptible to various inference attacks. This paper presents FLASS, a lightweight and decentralized federated learning framework that leverages additive secret sharing within a multi-server architecture. Each client encodes its local model update into additive shares and shares them with several non-colluding servers, which execute secure aggregation without gaining knowledge of individual contributions. FLASS does not rely on heavy-weight cryptography or a trusted authority. The security analysis and experiments demonstrate the effectiveness and efficiency of the suggested scheme. The analysis indicates that, while achieving the same accuracy as conventional FL schemes, FLASS ensures robust privacy protection with acceptable computational and communication overhead.

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.