Hugging Face Trending Papers July 27, 2026

Adaptive Data Admission and Retention for Streaming Federated Learning

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We study streaming federated learning with limited client memory, where newly generated training data incur time-varying sampling costs and must be selectively admitted and retained over time. We consider a joint server-side admission and client-side memory-management framework with the objective of minimizing the cumulative excess population risk under a sampling-cost budget and buffer constraints.

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benchmarks

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benchmarks

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Adaptive Data Admission and Retention for Streaming Federated Learning

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