## 2 Literature Review  
This section analyzes a number of significant research works that paved the way for the proposed framework. The growing popularity of Internet of Things (IoT) networks has generated considerable security concerns, prompting the creation of effective IDSs. Recent improvements in FL represent a viable approach, facilitating decentralized learning while safeguarding data privacy. This section examines current research initiatives that combine FL with DL. Several research works and their limitations with performance evaluation of the federated learning based architecture have been presented in the Table 1.  
[PERSON] et al. [24] initiated an IDS built on FL adopting a 1D convolutional neural network (CNN) to secure IoT networks. This framework enhances privacy through Diffie-Hellman key exchange, dynamic security, and homomorphic cryptography. [PERSON] et al. [25] introduced a federated deep neural network (FDNN) for detecting and preventing distributed denial-of-service (DDoS) integrating explainable artificial intelligence (XAI) in IoT networks. Their methodology employed FDNNs trained across three client devices over multiple rounds without sharing raw data. Furthermore, XGBoost was used with Shapley additive explanations (SHAPs) to select features, which improved model comprehensibility. This method successfully preserved robustness, scalability, and confidentiality while obtaining high detection accuracy. To further optimize FL-based IDSs, [PERSON] et al. [26] offered a semi-distributed FL model that clusters IoT devices and assigns a cluster head to reduce communication overhead. The model incorporated bidirectional LSTM (BiLSTM), long short-term memory (LSTM), and Wasserstein generative adversarial networks (WGANs) to enhance intrusion detection, particularly focusing on DDoS attacks in scenarios with scarce resources. Their evaluation implementing the CICIoT2023 data set revealed BiLSTM as the most efficient model due to its optimized size. Testing on WUSTL-IIoT-2021, Edge-IIoTset, and BoT-IoT further confirmed its superior detection accuracy. An unorthodox secure and authenticated structure built on federated learning employing Blockchain (SA-FLIDS) was initiated by [PERSON] et al. [27] to enhance security in advanced healthcare systems that are enabled via fog-IoMT. Experimental results using the CICIoT2023 and EdgeIIoTset data sets demonstrated the framework's strong resilience against adversarial attacks while preserving the confidentiality of the data and deducting the expenses of communication. [PERSON] et al. [28] addressed the dilemma of attack-class dispersion in FL-based IDSs by proposing FedMADE, an adaptive collaborative framework. FedMADE groups IoT gadgets based on the trends on the traffic and incorporates local approaches according to their significance to the entire evaluation. This approach significantly improved the accuracy of minority  
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attacks' classification to 71.07% compared to existing FL methods for non-IID data. Additionally, FedMADE exhibited robustness against poisoning attacks while incurring only a 4.7% latency overhead (5.03 s per iteration) contrasting with FedAvg, without uploading computational weigh on IoT gadgets. These research works collectively underline the efficacy of FL-based IDSs in intensifying cyber security within IoT networks. The combination of deep learning simulations, privacy-preserving techniques, and blockchain technology shows great promise for protecting modern IoT systems. However, issues such as communication overhead, data disparity, and adversarial resilience remain critical areas for further research and optimization. Deep learning (DL) and machine learning (ML) have become foundational approaches to constructing IDS, significantly improving network confidentiality by spotting various attacks and anomalies. Several ML techniques are widely used for their robustness and efficiency.