## 2 AI Techniques in Communication Networks
### Federated Learning for Privacy-Preserving Network Optimization  
Federated learning (FL) has emerged as a powerful technique for privacy-preserving machine learning, particularly in scenarios where data is distributed across multiple devices or locations. It enables collaborative learning without the need to centralize sensitive data, offering a promising solution to privacy and security challenges in network optimization [58]. In traditional centralized learning, raw data must be transferred to a central server for training, which raises privacy concerns and incurs significant data transmission costs. Figure 1: Accuracy vs. latency for ML/DL models in communication networks. FL addresses this by allowing each device or node in the network to train a local model based on its data, sharing only model updates (e.g., gradients or weights) with a central server for aggregation. This reduces data transmission, preserves privacy, and enables learning from decentralized data [59]. FL is especially well suited for distributed models where data are naturally decentralized, such as in mobile networks, Internet of Things (IoT) devices, and edge computing environments. In such networks, data generated by individual devices are often sensitive (e.g., user behavior, communication metadata, or location information), making it impractical or even undesirable to centralize the data. By using FL, privacy is preserved since raw data never leave the device, only local model updates are shared, and aggregation methods can be applied to further enhance security, such as secure aggregation and differential privacy techniques [60]. In the context of network optimization, federated learning enables decentralized collaboration for improving network performance, such as resource allocation, traffic routing, and congestion management. Each device (or node) in the network can train a local model that reflects its local conditions (e.g., traffic load, channel state, interference) and share only the resulting updates with a central server. The central server aggregates these updates and refines the global model, which is then shared back with all devices. This approach allows the network to learn from a large pool of distributed data while ensuring that individual device data remains private [61]. For example, in mobile networks, devices such as smartphones or base stations can use FL to collaboratively optimize tasks like radio spectrum allocation or load balancing. Rather than transferring sensitive information to a central entity, each device trains on its own network usage patterns and shares model updates. This process reduces communication costs and enhances privacy, as devices never share their raw data. Research has shown that federated learning can reduce data transmission by up to 30% compared to traditional centralized models while maintaining similar model accuracy. In some cases, FL models achieve up to 90% accuracy as illustrated in Figure 2, which is close to the performance of centralized models [62]. Applications in Network Optimization:  
Federated learning is being applied to various aspects of network optimization. One key application is in resource allocation and management in 5G and future 6G networks. FL can help optimize the allocation of spectrum resources across multiple devices without the need to centralize data. Each device or node can train its model to learn optimal resource management strategies, such as power control, interference mitigation, or scheduling, based on its local conditions. By sharing model updates, the network can benefit from collective learning without exposing sensitive information. This decentralized approach is particularly effective in heterogeneous networks, where different nodes may have distinct performance requirements or operating conditions [61]. Another promising area is traffic routing and congestion control. In traditional approaches, routing decisions are made by central controllers that rely on global network information.