## 2 Federated Learning Approaches  
The federated learning approaches published in this Special Issue include aggregation algorithms, layer regularization for effective training, cluster analysis, blockchain-based incentive mechanisms, influence study of unreliable participants, and security/privacy issues. **FL aggregation algorithms:** [PERSON] et al. [1] proposed an FL framework, called FedLA (Federated Lazy Aggregation), which reduces aggregation frequency to obtain high-quality gradients and improve robustness to non-independent and identically distributed (non-IID) training data. This framework is particularly used to deal with data heterogeneity. Based on FedAvg, FedLA allows more devices, which come from consecutive rounds, to be trained sequentially, without needing additional information about the devices. Due to sequential training, it performs well on both homogeneous and heterogeneous data, simply by discovering enough samples to aggregate them in time. Regarding the aggregation timing, this work proposes weighted deviation (WDR) as the change rate of the models to monitor sampling. Furthermore, the authors propose a FedLA with a Cross-Device Momentum (CDM) mechanism, called FedLAM, which alleviates the instability of the vertical optimization, helps the local models escape from the local optimum, and improves the performance of the global model in FL. Compared to benchmark algorithms (e.g., FedAvg, FedProx, and FedDyn), FedLAM performs best in most scenarios, achieving a 2.4-3.7% increase in accuracy compared to FedAvg on three image classification datasets. [PERSON] et al. [10] presented a novel federated aggregation algorithm, named FedGSA, which relies on gradient similarity for wireless traffic prediction. FedGSA addresses critical aspects in the optimization of cellular networks, including load balancing, congestion control, and the promotion of value-added services. This method incorporates several key elements: (i) utilizing a global data-sharing strategy to overcome the challenge of data heterogeneity in multi-client collaborative training within federated learning, (ii) implementing a sliding window approach to construct dual-channel training data, enhancing the model's capacity for feature learning, and (iii) proposing a two-layer aggregation scheme based on gradient similarity to enhance the generalization capability of the global model. This entails the generation of personalized models through a comparison of gradient similarity for each client model, followed by aggregation at the central server to obtain the global model. In the experiments conducted using two authentic traffic datasets, FedGSA outperformed the commonly used Federated Averaging (FedAvg) algorithm, delivering superior prediction results while preserving the privacy of client traffic data. **Layer regularization for effective training:** Federated learning is a widely used method for training neural networks on distributed data; however, its main limitation is the performance degradation that occurs when data are heterogeneously distributed. To address this limitation, [PERSON] et al. [3] proposed _FedCKA_, an alternative approach based on a more up-to-date understanding of neural networks, showing that only certain important layers in a neural network require regularization for effective training. In addition, the authors show that Centred Kernel Alignment (CKA) is the most appropriate when comparing representational similarity between layers of neural networks, in contrast with previous studies that used the L2-distance (FedProx) or cosine similarity (MOON). Experimental results reveal that FedCKA outperforms previous state-of-the-art methods in various deep learning tasks, while also improving efficiency and scalability.