A single global merge at the final step of decentralized SGD matches the convergence rate of parallel SGD while improving test accuracy under high data heterogeneity.
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6 Pith papers cite this work. Polarity classification is still indexing.
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SLIM-RL matches or exceeds TraceRL performance on MATH500, GSM8K, MBPP and HumanEval for diffusion LLMs by risk-budgeted random-masking RL without trajectory slicing.
BlockTrain partitions models into block-local diffusion objectives that train near end-to-end WikiText quality with one-block worker memory, real WAN transport, and one-sweep distributed serving.
AGDO improves dLLM reasoning performance by determining denoising order and emphasizing tokens based on attention-derived dependencies rather than random masking.
Local MixVR achieves communication complexity scaling only with number of workers M, independent of total samples N, and outperforms Minibatch Accelerated SGD when M is smaller than order N to the 1/4.
The survey introduces personalized federated intelligence (PFI) as a framework integrating federated learning and foundation models to support privacy-aware personalization of AI models.
citing papers explorer
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On the Surprising Effectiveness of a Single Global Merging in Decentralized Learning
A single global merge at the final step of decentralized SGD matches the convergence rate of parallel SGD while improving test accuracy under high data heterogeneity.
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SLIM-RL: Risk-Budgeted Random-Masking RL for Diffusion LLMs Without Trajectory Slicing
SLIM-RL matches or exceeds TraceRL performance on MATH500, GSM8K, MBPP and HumanEval for diffusion LLMs by risk-budgeted random-masking RL without trajectory slicing.
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Decentralised AI Training and Inference with BlockTrain
BlockTrain partitions models into block-local diffusion objectives that train near end-to-end WikiText quality with one-block worker memory, real WAN transport, and one-sweep distributed serving.
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Beyond Fully Random Masking: Attention-Guided Denoising and Optimization for Diffusion Language Models
AGDO improves dLLM reasoning performance by determining denoising order and emphasizing tokens based on attention-derived dependencies rather than random masking.
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Local MixVR: Breaking the Communication-Sample Dependence in Distributed Learning
Local MixVR achieves communication complexity scaling only with number of workers M, independent of total samples N, and outperforms Minibatch Accelerated SGD when M is smaller than order N to the 1/4.
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A Survey on Foundation Models for Personalized Federated Intelligence
The survey introduces personalized federated intelligence (PFI) as a framework integrating federated learning and foundation models to support privacy-aware personalization of AI models.