Secure Cooperative Gradient Coding: Optimality, Reliability, and Global Privacy

Abstract: This paper studies privacy-sensitive federated learning (FL) with unreliable communication, focusing on secure aggregation and straggler mitigation. While secure aggregation cryptographically reconstructs the global model without exposing client updates, random link failures disrupt its key coordination, degrading model accuracy. Moreover, unreliable communication can lead to objective inconsistency, causing the global model to converge to arbitrary, sub-optimal points far from the intended optimum. This paper proposes Secure Cooperative Gradient Coding (SecCoGC), a practical solution that achieves secure aggregation with arbitrarily strong privacy guarantees and robust straggler mitigation under unreliable communication. SecCoGC operates natively in the real field, making it directly applicable to practical deployments. To ensure equitable privacy protection across clients, we further introduce Fair-SecCoGC, an extension that enforces fairness in the level of privacy offered to all users. To conclude, this paper formally formulates the problem of secure aggregation in the real field and presents both general and computationally efficient key construction methods. Moreover, it provides a comprehensive privacy analysis under Local Mutual Information Privacy (LMIP) and Local Differential Privacy (LDP) across all protocol layers. Robustness and convergence properties are also rigorously analyzed. Finally, extensive simulations are performed across diverse network conditions and benchmark datasets to validate the effectiveness of the proposed methods. The results show that SecCoGC achieves strong robustness to unreliable communication under arbitrarily strong privacy guarantees. It outperforms existing privacy-preserving methods with performance gains of up to 20\%-70\%.