Federated learning (FL) is a machine learning (ML) technique that enables collaborative model training without sharing raw data, making it ideal for Internet of Things (IoT) applications where data are distributed across devices and privacy is a concern. Wireless Sensor Networks (WSNs) play a crucial role in IoT systems by collecting data from the physical environment. This paper presents a comprehensive survey of the integration of FL, IoT, and WSNs. It covers FL basics, strategies, and types and discusses the integration of FL, IoT, and WSNs in various domains. The paper addresses challenges related to heterogeneity in FL and summarizes state-of-the-art research in this area. It also explores security and privacy considerations and performance evaluation methodologies. The paper outlines the latest achievements and potential research directions in FL, IoT, and WSNs and emphasizes the significance of the surveyed topics within the context of current technological advancements.

Distributed and privacy-preserving federated learning (FL) has been associated with edge computing systems for developing intelligent IoT applications. However, collecting data individually in each FL node may result in non-independent and identically distributed (non-IID) training data, which can significantly impair FL performance. To address this, we propose a model transfer approach that allows a FL client to train its model on more datasets, collectively forming an IID virtual dataset. In the proposed approach, FL clients are classified as transfer-demand or aggregation-inclined based on their local model’s experienced data label distributions. Transfer-demand clients transfer their models to helper clients that can support their lacking labels, while aggregation-inclined clients have enough data labels in their training models, and thus, they participate in the model aggregation. However, two or more transfer-demand clients may contend with the same helper client. To resolve the contention, we apply the minimum-cost maximum-matching (MCMM) framework and integer linear programming to find the optimal solution. To minimize model transmission costs among FL clients, we use a Steiner tree-based solution to dynamically allocate a parameter server that aggregates the local models from clients. Finally, we perform extensive simulation experiments with different problems, and our proposed approach significantly outperforms baseline methods, in the case of MNIST achieves 99.02% accuracy and reduces the communication cost within the range of 20% to 66% and in the case of CIFAR-10 dataset, enhances the accuracy at least 24% and communication cost in the range of 29.48% to 63.09% in comparison with non-IID baseline approaches.