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This research aims to design a sustainable framework for optimizing distributed computing systems to enhance performance while minimizing energy consumption. Existing scheduling algorithms often
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models Uncertainty estimation and calibration Failure and out-of-distribution detection Adaptive and selective computation Model routing and dynamic inference Generalisation to unseen environments Embodied
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-scale AI model training, resource orchestration, cloud/edge computing, high-performance computing, or energy-efficient computing. Monash University strongly advocates diversity, equality, fairness and
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hospital or population often fail when applied elsewhere due to distributional shifts. Since acquiring new labeled data is often costly or infeasible due to rare diseases, limited expert availability, and
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paradigms rely on a fragile "closed-world" assumption: that the unlabeled pool perfectly reflects the distribution of the labelled seed set. In real-world deployments, this is rarely true. Data streams
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of Machine Learning (ML) models across large-scale distributed systems. Leveraging advanced AI and distributed computing strategies, this project focuses on deploying ML models on real-world distributed
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at one time. In non-stationary environments on the other hand, the same algorithms cannot be applied as the underlying data distributions change constantly and the same models are not valid. Hence, we need
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management, distributed computing, and energy-aware computing, preparing them for impactful roles in industry and research. Key Components and Example Scenarios Predictive Resource Allocation and Load
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Background and Motivation Modern deep learning models have achieved remarkable success in computer vision and natural language processing. However, they typically produce overconfident predictions
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Federated learning (FL) is an emerging machine learning paradium to enable distributed clients (e.g., mobile devices) to jointly train a machine learning model without pooling their raw data into a