Existing solutions for resource allocation in Zero Trust (ZT) SDN networks treat security, resilience, and efficiency separately; centralised approaches violate data privacy; deep reinforcement learning (DRL) lacks trust dynamics; and federated learning (FL) has not incorporated graph neural networks (GNNs) or failure resilience. These limitations motivate a federated GNN-DRL framework that preserves data locality while jointly optimising trust, resilience, and performance. Each domain trains a local GNN-DRL agent with a GNN encoder for topology awareness and a hybrid DRL module (Deep Q-Network for discrete failover actions and Soft Actor-Critic for continuous bandwidth tuning) under a stochastic trust evolution modelled by stochastic differential equations (SDEs) with a reflection mechanism to ensure well-posedness. Three pseudo-code algorithms detail client-side training (Algorithm 1), server-side q-fair aggregation (Algorithm 2), and local gradient updates (Algorithm 3). Extensive simulations on a 100-node topology divided into five domains demonstrate that: (i) under low-to-moderate failures (≤20%), trust violations remain below 1.8%, and even under severe failures (40%), violations stay at 4.2% (within the 5% ZT boundary); (ii) recovery time is reduced by 53%; (iii) throughput under failures improves by 32%; (iv) compromise resistance reaches seven nodes (vs. three centralised); (v) attack surface shrinks to four nodes (vs. 98 baseline); and (vi) lateral movement containment attains 98%. The federated framework approaches centralised performance while preserving data locality, offering a practical and secure solution for multi-domain ZT, SDN networks.
Sibiya et al. (Fri,) studied this question.