# Federated Reinforcement Learning in IoT: Applications, Opportunities and Open Challenges
## 3 Methodology
### FRL Applications in the IoT in Terms of Security
The authors use federated deep reinforcement learning (fDRL) to train an offloading policy that can adapt to IoT environment changes while considering privacy requirements. This approach allows for better control over the trade-off between offloading performance and privacy. The experimental results show that the C-fDRL approach outperforms traditional offloading methods in terms of both efficiency and privacy protection. The authors conclude that using fDRL in IoT offloading can deliver a more secure and efficient answer while preserving the privacy of sensitive data. [PERSON] et al. [98] present a new method called FL2S, designed to provide secure data sharing in IoT systems. The proposed approach utilizes federated deep reinforcement learning (FDRL) to create a policy that can adapt to IoT environment changes. This policy is designed to enhance data-sharing security in IoT systems. The proposed approach, FL2S, employs fDRL to create a data-sharing policy that adapts to the context of each IoT device and the requirements of each sharing scenario. The approach provides a more flexible and secure way of managing data sharing in IoT systems, considering privacy and security risks. Experimental results demonstrate that the fDRL-based approach outperforms traditional methods regarding both efficiency and security. The authors conclude that using fDRL in IoT data sharing can lead to a more secure and efficient solution while preserving the privacy of sensitive data. [PERSON] et al. [99] suggest a new method to solve security issues related to resource allocation in privacy-preserving EdgeIoT, which involves using DRL to train a policy to make online resource allocation decisions based on the context of each edge device. They suggest a novel FL-enabled twin-delayed deep deterministic policy gradient (FL-DLT3) framework to balance privacy preservation and resource utilization. This approach is designed to optimize resource allocation by learning from experience, considering the context of each edge device, and balancing privacy preservation with the efficient use of resources. The proposed approach can potentially enhance the efficiency and security of resource allocation by using deep reinforcement learning, federated learning, differential privacy, and access control mechanisms. According to the numerical results, for the better prediction of time-varying data size, bandwidth, channel gain, and remaining energy of IoT devices, the authors developed a new state characterization layer based on LSTMs in FL-DLT3. The FL-DLT3 that has been suggested achieves rapid convergence, taking fewer than 100 iterations. By comparing it to the current leading benchmark, FL shows a 51.8% improvement in accuracy-to-energy consumption. [PERSON] et al. [100] present a new framework based on multi-task federated RL for learning multiple tasks while protecting against adversarial attacks. The suggested framework utilizes federated RL to allow agents to learn from one another while defending against adversaries. The multi-task RL formulation ensures that the agents can learn multiple tasks while assessing the trade-off between task performance and adversary defense. This framework can enhance learning efficiency in multi-agent systems in the presence of adversaries. The effectiveness of the proposed method is assessed through simulated experiments, revealing that the agents can effectively acquire knowledge for multiple assignments and protect themselves from adversarial attacks. The authors proposed a new attack method called AdAMIng that considers the aggregation operator used in federated RL. Further, they proposed modifying the conventional federated RL algorithm called ComA-FedRL to manage the issue of adversaries in the multi-task federated RL problem. The solution incorporates FRL, differential privacy, and Byzantine-resilient aggregation mechanisms to ensure the privacy and security of local models and detect malicious behavior.