Key management is a critical aspect of securing Wireless Sensor Networks (WSNs), ensuring confidentiality, integrity, and authentication of the communicated data. This paper presents a comprehensive study on key management techniques in WSNs, focusing on both static and dynamic methods. Static key management involves pre-distributing keys to sensor nodes before deployment, offering simplicity and reduced communication overhead but facing challenges in scalability and resilience against node capture attacks. Dynamic key management, on the other hand, entails periodic updates and re-establishment of keys, enhancing security through adaptability and resilience but at the cost of increased communication and computational overhead. We compare and contrast various static methods, such as random key pre-distribution and pairwise key establishment, with dynamic methods like re-keying protocols and key refreshment techniques. Our analysis highlights the strengths and weaknesses of each approach in terms of security, efficiency, and practicality for different WSN applications. By providing a detailed exploration of key management schemes, this paper aims to guide the design and implementation of secure and efficient key management protocols tailored to the unique constraints and requirements of WSNs.

With the advent of data driven era, new challenges have emerged that can hinder their efficient and reliable utilization. At the center of this era is data. However, public, and unrestricted access to data may pose privacy and security threats. Federated learning methods have proposed to address this problem. While the main concern in federated learning has been preserving privacy of data and maintaining the efficiency of centralized learning methods, the structure and distribution of data has not been studied sufficiently. In this paper, we present a federated machine learning approach capable of accommodating imbalanced datasets. Our method dynamically adjusts the learned parameters to prevent biased classifications. Our empirical results indicates that the proposed method managed to improve accuracy by 22%.