## 5 ML Intersection with 6G Enabling Technologies
### ML and Blockchain Integration for Future 6G Networks  
BC has proven effective in strengthening the security and privacy of wireless communication networks; particularly, for the future 6G network, BC guarantees the foundation for trust between various network services and applications [161]. Blockchain has been effectively incorporated into FL to develop a low-latency and high-reliability wireless system [162]. The authors proposed a blockchain-based federated learning (BlockFL) architecture for the secure training and updating of data. By utilizing a consensus mechanism in blockchain, the system ensured an optimal block generation rate which enabled better communication, computation, and consensus delays. [PERSON] et al. [163] explore the convergence of digital twins, edge networks, blockchain, and FL-based ML technologies for efficient communication. After extensive numerical simulations, the results showed that the integrated blockchain and federated learning scheme yielded better efficiency and accuracy while the DT and MEC integration enhanced user and resource scheduling. In a similar study, an integrated approach was also developed for collaborative computing to enable low latency and improved efficiency for 6G networks [164]. DT lowers the latency and enhances reliability while blockchain assists in the overall security and efficiency of DT in the 6G network. The results showed that improvements in latency and learning convergence were achieved. Dynamic resource sharing (DRS) has been developed by combining ML and blockchain technology for wireless 6G networks [165]. ML allowed for the optimization of user profit ratio and throughput, while blockchain technology improved the distribution, security, and automation of the network. Three approaches were considered by the authors and the results indicated that the DRL outperformed the other two methods in improved profit ratio. See Figure 5. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Ref**} & \multicolumn{2}{c}{**ML**} & \multirow{2}{*}{**Application**} & \multicolumn{1}{c}{**Outcome**} \\ \hline
[154] & LSTM & Offloading & The integrated approach showed improvement in terms of task success ratio and system computation rate. \\ \hline
[155] & MADRL & JTDPA & The new approach achieved an optimized JTDPA policy with faster convergence speed compared to the other methods. \\ \hline
[156] & MFTRPO & Trajectory optimization & The developed approach achieved superior energy efficiency, coverage, and fairness compared to the other methods. \\ \hline
[160] & DEO & Energy optimization & The developed approach obtained better emission power to the edge device. \\ \hline
[159] & DQN & Path planning and obstacle avoidance & The developed approach achieved improvement in computation time and path length by 50\% and 30\%, respectively. \\ \hline
[158] & ESN & Trajectory and power optimization & The proposed approach improved the prediction accuracy, and throughput gains by over 17\%. \\ \hline
[157] & RL & Trajectory optimization & The developed approach obtained superior performance in terms of energy consumption, path length, flying time, and velocity. \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Summary of ML and UAV integration for optimized 6G wireless networks. A similar approach has been developed for secure content caching for next-generation wireless networks [166], the authors explored ML integration with BC for four different network management schemes ranging from spectrum sharing, content caching, energy trading, and computation offloading. By applying the advanced DRL approach and BC, their proposed method achieved better cumulative average system utility compared to the existing benchmark caching approaches. While other studies have explored novel BC consensus protocols, [PERSON] et al. [113] considered a learning-based consensus protocol (proof of learning (PoL)) integrated with DRL for effective joint resource allocation application.