In federated learning, unstable clients may disrupt the training process of the global model through data pollution or malicious behavior. Traditional defense methods typically focus on excluding these clients, but overlook the fact that the data generated by unstable clients can also provide valuable training signals for the model. To address this issue, a federated adversarial training with an enhanced adaptability method, Fed-ATEA, which utilizes adversarial examples generated by unstable clients to enhance the robustness of the global model is proposed. This framework allows for the incorporation of adversarial samples into the training process of trusted clients without excluding unstable ones, thereby improving the stability and robustness of the model. By dynamically adjusting training strategies, Fed-ATEA maximizes the utilization of beneficial signals from unstable clients while effectively mitigating their negative influences. Experimental results show that, compared to other federated learning methods, Fed-ATEA demonstrates stronger stability and robustness in handling attacks and noise interference.