# Federated Reinforcement Learning in IoT: Applications, Opportunities and Open Challenges
## 3 Methodology
### FRL Applications in the IoT in Terms of Security
In addition, they use FL to train the 2 Ts-DRL model in a decentralized manner to safeguard data privacy on edge devices.Although the paper does not mention any particular security concerns, it does address the overall security challenges facing MEC networks. Such networks are susceptible to several security threats, such as attacks on edge devices, data breaches, and denial-of-service attacks. The proposed approach uses DRL and FL to optimize resource allocation and management to mitigate these threats, enhancing network performance and lowering the risk of attacks and other security threats. While FRL has many potential applications, current research must address two crucial problems. Firstly, there needs to be more theoretical analysis of the convergence of FRL algorithms. Secondly, the recent works do not consider the effect of random system failures or adversarial attacks on the execution of FRL. [PERSON] et al. [105] propose the first FRL framework that ensures convergence and is resilient to the failure or malicious behavior of up to half of the participating agents.The proposed approach called FT-FRL with theoretical guarantees has been presented to improve the security of FRL. This can effectively address the fault tolerance issue and enhance FRL's overall security. This framework uses a Byzantine fault-tolerant algorithm that allows the agents to exchange information securely while ensuring that malicious agents cannot disrupt the learning process. The FT-FRL algorithm is compared with existing reinforcement learning algorithms, demonstrating that it performs better in system performance and accuracy in a decentralized environment. In reference [86], the authors present an FRL architecture in which each agent operates independently on their respective IoT device and shares their learning experience with other agents in a decentralized way. This scheme addresses the security concerns related to training control policies for IoT devices that arise due to scalability. Leveraging FRL to optimize the training process can significantly improve the overall security of these devices. This technique lets agents learn from each other's experiences without sharing raw data, enhancing the system's general performance. Using this FRL architecture, the data privacy on the IoT devices is maintained, and the agents can cooperate effectively to acquire optimal control policies for the given task. They combine the actor-critic proximal policy optimization (actor-critic PPO) algorithm into each agent in the suggested collaborative framework. They also present an effective method for exchanging gradients and transferring model parameters to the agent. The main focus of the authors of [106], is to tackle the security and privacy concerns in FL, where clients provide their data to a central server for model training. The proposed solution is to enhance the security and privacy of FL by carefully choosing reliable clients to participate in the learning process. They offer a novel method for determining trustworthy and reliable clients in federated learning systems. They implement their proposed method in the healthcare sector, specifically for detecting COVID-19 using IoT devices. They use a dataset of COVID-19 chest X-rays and apply the federated learning framework with the proposed client selection mechanism to train a model for COVID-19 detection. Finally, Table 1 provides a summary of all the works that were reviewed, taking into account their areas of study, the technologies used, and the objectives pursued.