FORTRESS-FL: Byzantine-robust and privacy-preserving federated orchestration for next-generation networks

The transition to 6G and Open RAN (O-RAN) necessitates intelligent orchestration across multi-operator networks, yet this collaboration introduces severe security and privacy risks. Malicious operators may poison global models through adaptive attacks, while the exchange of raw gradients threatens data sovereignty. In this paper, we propose FORTRESS-FL , a robust and privacy-preserving federated learning framework designed for secure cross-domain orchestration. At its core is the TrustChain protocol, which synergizes a commit-then-reveal scheme to prevent adaptive manipulation, unsupervised spectral clustering for Byzantine detection, and a dynamic reputation system to isolate malicious actors. Furthermore, we integrate an adaptive Differential Privacy (DP) mechanism to rigorously protect operator data. Extensive evaluation on a real-world financial fraud dataset demonstrates that FORTRESS-FL achieves 100% detection accuracy against sign-flip attacks with 30% Byzantine adversaries, preventing the model divergence observed in standard baselines. Scalability tests confirm linear complexity with respect to the number of operators, validating the framework’s feasibility for large-scale, real-time network orchestration.