Supports Byzantine failures, network partitioning, cascading failures, and hardware-level faults (e.g., bit flips, NIC drops) to test system resilience in highly volatile environments.
Train reinforcement learning agents to dynamically allocate compute resources and bandwidth based on workload characteristics and real-time network constraints.
Model heterogeneous edge-cloud hierarchies with constrained devices, intermittent connectivity, and strict latency bounds to represent modern IoT setups and federated learning architectures.
Includes dedicated modules for simulating adversarial network attacks, intrusion detection systems, gradient leakage, and secure enclave training environments across the cluster.