AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This paper considers the practical setting where the learner keeps a small proxy dataset, and proposes a dynamic, influence-aware client selection framework that estimates each client's potential utility to the learner's optimization objective using proxy influence signals on a learner-specific proxy set.
Results show that DART-FL dynamically adapts the inference-training resource split to time-varying inference demand and shifts the learning progress of high-demand tasks toward their burst periods, improving model accuracy when those tasks are frequently requested while maintaining comparable long-term multitask performance.
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