## 2 Related Work
It also interfaces with PyTorch, enabling training using surrogate gradient techniques and machine learning optimization methods. The authors also analyze the impact of weight quantization and drift in memristive synapses when deploying the trained networks to neuromorphic hardware. Also, regarding SNNs in the context of multi-hop graph pooling adversarial networks for cross-domain remaining useful life prediction, a distributed federated learning perspective can offer valuable insights. Federated learning with SNNs presents an efficient framework that considers data distribution among clients, stragglers, and gradient noise [36]. This approach not only enhances efficiency but also addresses the sensitivity of the framework to various data distribution scenarios. Analyzing the performance of SNNs in different network topologies and communication protocols is crucial. A comprehensive analytical model can provide insights into the behavior of SNNs in 3D mesh NoC architectures [37]. Understanding how SNNs interact within such structures can lead to optimized designs for efficient information processing and routing. Adaptive approaches are essential in the realm of federated learning, especially in resource-constrained edge computing systems. Adaptive federated learning in edge computing systems addresses the challenge of learning model parameters from distributed data without centralizing raw data [38]. This decentralized approach aligns well with the distributed nature of federated learning and can be beneficial in scenarios where data privacy and network bandwidth are critical concerns. Moreover, the concept of federated learning in mobile edge networks is pivotal. Traditional DNN training often relies on centralized cloud servers, which may not be suitable for edge computing environments [39]. Federated learning offers a decentralized alternative to improve model training efficiency while respecting data locality and privacy constraints. Incorporating SNNs in federated learning frameworks introduces novel paradigms for on-chip learning in embedded devices. Federal SNN distillation, for instance, presents a low-communication-cost federated learning framework tailored for SNNs [40]. This framework leverages the power efficiency and privacy advantages of SNNs, making them suitable for edge devices with limited resources. Integrating advanced entropy theory in machine learning can enhance the learning performance of SNNs. Heterogeneous ensemble-based spike-driven few-shot online learning explores new perspectives to improve SNN learning capabilities [41]. By incorporating entropy theory, SNNs can achieve better learning outcomes, which is crucial for applications requiring efficient and adaptive neural networks. Additionally, incorporating the Levenberg-Marquardt backpropagation neural network (LMBPNN) can further enhance the predictive accuracy of the models [42]. The LMBPNN has demonstrated high accuracy rates in various applications, such as seizure detection, underscoring its potential to significantly improve the accuracy and reliability of predictive models in the domain of remaining useful life prediction. Furthermore, a comparison of classification accuracies across different methodologies, including unsupervised k-means clustering, linear and quadratic discriminant analysis, radial basis function neural network, and LMBPNN, underscores the superior performance of LMBPNN in specific applications [43]. This comparison suggests that LMBPNN could be a suitable choice for augmenting the predictive capabilities of SNNs within the specific context of multi-hop graph pooling adversarial networks for cross-domain remaining useful life prediction. Moreover, a study showcases the efficacy of the Levenberg-Marquardt algorithm in training a Wavelet Neural Network (WNN) model for state of charge estimation in lithium-ion batteries [44].