EdgeMLBalancer uses epsilon-greedy switching among four edge object-detection models, driven by CPU usage and confidence, and reports better accuracy and fairness than two baselines in a single 30-minute smartphone trial.
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EdgeMLBalancer: A Self-Adaptive Approach for Dynamic Model Switching on Resource-Constrained Edge Devices
EdgeMLBalancer uses epsilon-greedy switching among four edge object-detection models, driven by CPU usage and confidence, and reports better accuracy and fairness than two baselines in a single 30-minute smartphone trial.