## 5 Future Trends, Challenges, and Opportunities
### Exploring Different Learning Approaches  
Most reported approaches for enhancing RIS-assisted wireless networks are based on DL. Several frameworks have begun to incorporate RL and FL as well. However, there are ML algorithms that have not yet been widely explored.  
Transfer learning (TL) exploits a trained model's learning ability and applies this knowledge to a different but related task. We stated before the importance of sharing databases and source code, but sharing the trained ML model and making transfer learning is another area of opportunity that can offer the following benefits:  
* Carbon footprint and environmental sustainability: The computational cost of the training stage involves energy consumption. However, transfer learning allows researchers to enhance trained ML models to reduce training time and resource consumption.
* Ensure data efficiency: If the model has been trained on large datasets, it can be fine-tuned on smaller datasets. Here, the challenge is to acquire extensive labeled data.
* Generalization of models: Transfer learning allows the development of models trained from different sources and tasks to be generalized. Generalized models could perform well on different communications scenarios and new paradigms such as NOMA [113] or cell-free schemes [114].  
Distributed machine learning (DML) is a decentralized ML approach which has been applied to RIS-assisted networks. Unlike FL, each device training is initialized independently since no additional devices are participating in the network. On the other hand, in DML, it is possible to exchange parameters and gradients among nodes without coordination or synchronization techniques [115]. Adding graph neural networks (GNN) to DML optimization can reveal the relations among the wireless network elements. However, DML sophisticated techniques have not yet been studied [28; 116].  
Extreme machine learning (EML) is a new paradigm in neural networks [117]. EML model training is faster, and the number of hyperparameters is reduced compared to DL, making tuning more straightforward. Applications of EML can benefit RIS-assisted IoT schemes since they require less memory than conventional DL, making EML suitable for those IoT devices with hardware constraints.  
Transformer architectures: With the rise of large language models and natural language processing leveraged by applications such as ChatGPT, transformer architectures have recently become a subject of interest. Sixth-generation intelligent communications based on transformers have been proposed as a promising approach for future networks [118; 119; 120].  
TinyML: Most current works rarely consider complex computation, massive storage, and consumed energy issues when training and deploying an ML framework to enhance RIS-aided wireless systems. Here, tinyML presents hardware-efficient solutions for beamforming, channel estimation, and federated learning problems [121; 122].  
Swarm intelligence methods: The enhancement of communication systems has been addressed by swarm methods for beamforming, resource allocation, and other related issues [123]. This area has been considered a subset of AI to obtain high-quality solutions for optimizing wireless systems. The combination of DL and swarm intelligence is a potential solution for the optimization of next-generation networks.