A Trustworthy Federated Learning Framework for Joint Privacy and Byzantine Resilience

Federated Learning (FL) has emerged as a promising paradigm for collaborative model training without direct data sharing. However, existing frameworks struggle to simultaneously ensure strong privacy protection and robustness against adversarial participants. Although numerous robust aggregation algorithms can mitigate Byzantine attacks and various privacy-preserving approaches have been developed, achieving both objectives within a unified and efficient framework remains challenging. To address this compatibility gap, we propose a generic privacy-preserving FL framework that integrates robust aggregation with cryptographic privacy guarantees through secure two-party computation under a dual-server architecture. In our design, two non-colluding servers collaboratively execute the aggregation process, ensuring that sensitive client updates remain confidential while enabling the application of diverse robust aggregation strategies. The framework is flexible and supports a wide range of aggregation algorithms, such as Multi-Krum, Median, and Mean-based methods, under a consistent security model. We implement the proposed system and conduct extensive experiments on multiple benchmark datasets. Experimental results demonstrate that our framework preserves the effectiveness of existing robust aggregation algorithms while maintaining acceptable runtime and communication overhead compared with standard FL baselines. These findings confirm that the proposed approach provides a practical and balanced solution to the dual challenges of privacy protection and Byzantine robustness, offering a versatile foundation for secure and trustworthy FL in adversarial environments.