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...
A Novel Nomad Migration-Inspired Algorithm for Global Optimization
Journal Article
Lin, N., Fu, L., Zhao, L., Hawbani, A., Tan, Z., Al-Dubai, A., & Min, G. (2022)
A Novel Nomad Migration-Inspired Algorithm for Global Optimization. Computers and Electrical Engineering, 100, Article 107862. https://doi.org/10.1016/j.compeleceng.2022.107862
Nature-inspired computing (NIC) has been widely studied for many optimization scenarios. However, miscellaneous solution space of real-world problem causes it is challenging t...
Deep Learning in Mobile Computing: Architecture, Applications, and Future Challenges
Journal Article
Yang, X., Tan, Z., & Luo, Z. (2021)
Deep Learning in Mobile Computing: Architecture, Applications, and Future Challenges. Mobile Information Systems, 2021, 1-3. https://doi.org/10.1155/2021/9874724
No abstract available.