# Federated Reinforcement Learning in IoT: Applications, Opportunities and Open Challenges
## 3 Methodology
### FRL Applications in the IoT in Terms of Security
[102] present a solution for quality of service (QoS) and privacy-aware routing in the context of the 5G-enabled IIoT. The approach considers the constraints of the IIoT environment and the communication requirements to create an efficient routing mechanism that ensures QoS while protecting user privacy. This approach aims to optimize routing decisions while ensuring that the privacy of the data transmitted is preserved. The proposed approach utilizes an FRL method to optimize the QoS while ensuring the confidentiality of the user. The experiment results show that the quality of service and privacy-aware routing (QoSPR) protocol can serve as a routing method that considers data privacy concerns, effectively reducing the average and maximum latency and ensuring optimal load balancing in 5G-enabled IIoT networks. The authors of [22] propose a new framework called MARL-FRL to improve the security of ICPS. It encourages agents to work in the system's best interest, thereby reducing the risks of security threats and malicious attacks. They introduce the MA-FRL algorithm, which aims to address the problem of nonstationarity that arises due to frequent interaction between devices in FL without compromising privacy by sharing sensitive information. The suggested methodology requires several agents to interact with each other and acquire knowledge from their shared experiences, enabling the system to adjust to evolvingcircumstances and enhance its security measures. The authors illustrate the efficacy of their suggested method through simulations, and the results indicate that the suggested MARL-based mechanism can improve the security of intelligent cyber-physical systems by providing a more robust and adaptive approach to managing incentives. Virtual network functions (VNFs) are a collection of VNFs linked in a particular sequence to make different network services compatible and flexible. The application of network function virtualization (NFV) enables these VNF groups to work together in a federated manner. Nevertheless, meeting the rising demands of network services through NFV execution can be challenging, mainly because of the fixed orchestration of service function chains (SFCs). NFV is necessary to obtain compatibility and scalability for different network services in service function chains (SFCs). The NFV execution is difficult to achieve the ever-increasing conditions of network services, especially due to the static orchestrations of SFCs. To address this challenge, the authors of [103] present a scheme based on scalable SFC orchestration (SSCO) for NFV-enabled networks via FRL. SSCO has unique features that differentiate it from the prior work, such as (1) it allows for training an international education prototype that utilizes time-variant regional sample investigations. This framework makes it possible to orchestrate scalable benefit function chains (SFC) on a large scale while also ensuring that stakeholder data is kept private. (2) The SSCO approach permits parameter updates between local clients and the cloud server solely at the start and finish of every episode. This guarantees that distributed clients can enhance the model while reducing communication expenses. This approach is designed to improve the security of NFV-enabled networks by optimizing SFC orchestration. This can enhance network performance while minimizing the risk of security threats and attacks. Similar to [88; 95], [PERSON] et al. in [104] discussed the 5G network. They proposed a framework combining DRL and FL to allow intelligent resource management in 5G ultra-dense networks. The framework utilizes a hierarchical architecture with global and local controllers to handle resources at various timescales. It seeks to enhance resource allocation and task offloading in a multi-access edge computing environment. They created a unique and authentic direction to DRL called the \"two-timescale deep reinforcement learning (2 Ts-DRL) approach\". This method contains two learning processes: one on a fast-timescale and another on a slow-timescale.