Recent research has demonstrated that Non-Identically Distributed (Non-IID) data can
negatively impact the performance of global models constructed in federated learning. To …

Software quality is critical, as low quality, or “Code smell,” increases technical debt and
maintenance costs. There is a timely need for a collaborative model that detects and …

Modern systems and applications generate and consume an enormous amount of data from
different sources, including mobile edge computing and IoT systems. Our ability to locate …

Swarm intelligence (SI) algorithms represent a class of Artificial Intelligence (AI) optimization
metaheuristics used for solving complex optimization problems. However, a key challenge in …

Digital forensics is a rapidly growing sub-domain of forensic science, primarily due to
technological advancements and their integration into everyday life. In this paper, our study …

The Metaverse, a 3-dimensional virtual realm mirroring real-world objects, promises
transformative experiences. Its potential is tempered by data collection, confidentiality, and …

In the rapidly evolving machine learning (ML) and distributed systems realm, the escalating
concern for data privacy naturally comes to the forefront of discussions. Federated learning …

Federated Learning (FL) has emerged as a transformative paradigm in machine learning,
enabling decentralized model training while preserving data privacy across multiple clients …