Federated Learning (FL) has recently been applied to the parameter-efficient fine-tuning of Large Language Models (LLMs).

However, recent findings indicate that small, unnoticeable perturbations of graph structure can catastrophically reduce performance of even the strongest andmost popular Graph Neural Networks (GNNs).

We identify three key challenges when using learned dual variables in a primal-dual algorithm.