Saifur Rahman Sabuj

The Internet of Things (IoT) is a network of electrical devices that are connected to the Internet wirelessly. This group of devices generates a large amount of data with information about users, which makes the whole system sensitive and prone to malicious attacks eventually. The rapidly growing IoT-connected devices under a centralized ML system could threaten data privacy. The popular centralized machine learning (ML)-assisted approaches are difficult to apply due to their requirement of enormous amounts of data in a central entity. Owing to the growing distribution of data over numerous networks of connected devices, decentralized ML solutions are needed. In this paper, we propose a Federated Learning (FL) method for detecting unwanted intrusions to guarantee the protection of IoT networks. This method ensures privacy and security by federated training of local IoT device data. Local IoT clients share only parameter updates with a central global server, which aggregates them and distributes an improved detection algorithm. After each round of FL training, each of the IoT clients receives an updated model from the global server and trains their local dataset, where IoT devices can keep their own privacy intact while optimizing the overall model. To evaluate the efficiency of the proposed method, we conducted exhaustive experiments on a new dataset named Edge-IIoTset. The performance evaluation demonstrates the reliability and effectiveness of the proposed intrusion detection model by achieving an accuracy (92.49%) close to that offered by the conventional centralized ML models’ accuracy (93.92%) using the FL method.

The federated learning‐based symbol detection technique has attracted much attention recently due to its application of processing data at the network edge (ie, mobile user equipment) in a distributed manner. In this regard, the federated receiver is presently being promoted as a replacement for and improvement on the conventional model‐based receiver. This paper proposes an average pooling with compensation layer and an average pooling with dense layers‐added federated receiver for symbol detection in a downlink fading channel. In average pooling with a compensation scheme, the average of the input data is extracted and then ripples of the data are minimized by multiplying the weight coefficient in the compensation layer. In average pooling with dense scheme, two pooling schemes, such as average pooling and max‐pooling, are incorporated into dense layers with various activation functions. Simulation results are provided to demonstrate that the proposed compensation layer‐added federated receiver outperforms the conventional federated receiver.