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- The University of MelbourneAU136 papers
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60 papers · 1 filter
On the Push-Based Asynchronous Federated Learning: A Bias-Correction Aggregation Approach
Jiahui Bai, Hai Dong, A. K. Qin
Asynchronous decentralized federated learning (ADFL) eliminates central coordination and global synchronization, making it attractive for large-scale and heterogeneous systems. How…
EventADL: Open-Box Anomaly Detection and Localization Framework for Events in Cloud-Based Service Systems
Luan Pham, Victor Nicolet, Joey Dodds +2
Anomaly detection and localization (ADL) is critical for maintaining reliability and availability in cloud systems. Recent ADL developments focus on metric and log data, leaving ev…
WinFLoRA: Incentivizing Client-Adaptive Aggregation in Federated LoRA under Privacy Heterogeneity
Mengsha Kou, Xiaoyu Xia, Ziqi Wang +4
Large Language Models (LLMs) increasingly underpin intelligent web applications, from chatbots to search and recommendation, where efficient specialization is essential. Low-Rank A…
Approximation Algorithm for Constrained -Center Clustering: A Local Search Approach
Chaoqi Jia, Longkun Guo, Kewen Liao +3
Clustering is a long-standing research problem and a fundamental tool in AI and data analysis. The traditional k-center problem, a fundamental theoretical challenge in clustering,…
Energy and Memory-Efficient Federated Learning With Ordered Layer Freezing
Ziru Niu, Hai Dong, A. K. Qin +2
Federated Learning (FL) has emerged as a privacy-preserving paradigm for training machine learning models across distributed edge devices in the Internet of Things (IoT). By keepin…
Predicted-occupancy grids for vehicle safety applications based on autoencoders and the Random Forest algorithm
Parthasarathy Nadarajan, Michael Botsch, Sebastian Sardina
In this paper, a probabilistic space-time representation of complex traffic scenarios is predicted using machine learning algorithms. Such a representation is significant for all a…