Can Federated Models Be Rectified Through Learning Negative Gradients?
Conference Proceeding
Tahir, A., Tan, Z., & Babaagba, K. O. (2024)
Can Federated Models Be Rectified Through Learning Negative Gradients?. In Big Data Technologies and Applications (18-32). https://doi.org/10.1007/978-3-031-52265-9_2
Federated Learning (FL) is a method to train machine learning (ML) models in a decentralised manner, while preserving the privacy of data from multiple clients. However, FL is...
2-Dimensional Outline Shape Representation for Generative Design with Evolutionary Algorithms
Conference Proceeding
Lapok, P., Lawson, A., & Paechter, B. (2019)
2-Dimensional Outline Shape Representation for Generative Design with Evolutionary Algorithms. In H. Rodrigues, J. Herskovits, C. Mota Soares, A. Araújo, J. Guedes, J. Folgado, …J. Madeira (Eds.), EngOpt 2018 Proceedings of the 6th International Conference on Engineering Optimization, (926-937). https://doi.org/10.1007/978-3-319-97773-7_80
In this paper, we investigate the ability of genetic representation methods to describe two-dimensional outline shapes, in order to use them in a generative design system. A s...
Security, privacy and safety evaluation of dynamic and static fleets of drones
Conference Proceeding
Akram, R. N., Markantonakis, K., Mayes, K., Habachi, O., Sauveron, D., Steyven, A., & Chaumette, S. (2017)
Security, privacy and safety evaluation of dynamic and static fleets of drones. In 2017 IEEE/AIAA 36th Digital Avionics Systems Conference (DASC),https://doi.org/10.1109/dasc.2017.8101984
Interconnected everyday objects, either via public or private networks, are gradually becoming reality in modern life -- often referred to as the Internet of Things (IoT) or C...