From the Ball-proximal (Broximal) Point Method to Efficient Training of LLM

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Scale Learning and Reasoning Across Heterogeneous Gradients and Semantics

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"Federated Reinforcement Learning: Statistical and Communication Trade-offs

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Understanding Outer Optimizers in Local SGD: Learning Rates, Momentum, and Acceleration

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The Many Faces of Heterogeneity: Federated, Continual, and Modular Learning

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Symbiotic Relations between Decoupled Training, Optimization, and Federated Learning

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Privacy Amplification from Structured Algorithmic Randomness

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Federated, Synthetic, Personalized: Heterogeneity Here or There?

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Privacy of Decentralized Machine Learning

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Exact Unlearning of Finetuning Data via Model Merging at Scale

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