338 citations · 406 across the 6 of their papers we have counts for
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cs.LG2023★ 30 cited
ElasticTrainer: Speeding Up On-Device Training with Runtime Elastic Tensor Selection
Kai Huang, Boyuan Yang, Wei Gao
On-device training is essential for neural networks (NNs) to continuously adapt to new online data, but can be time-consuming due to the device's limited computing power. To speed…
cs.LG2023
Tackling Intertwined Data and Device Heterogeneities in Federated Learning with Unlimited Staleness
Haoming Wang, Wei Gao
Federated Learning (FL) can be affected by data and device heterogeneities, caused by clients' different local data distributions and latencies in uploading model updates (i.e., st…
cs.LG2023★ 4 cited
Towards Green AI in Fine-tuning Large Language Models via Adaptive Backpropagation
Kai Huang, Hanyun Yin, Heng Huang +1
Fine-tuning is the most effective way of adapting pre-trained large language models (LLMs) to downstream applications. With the fast growth of LLM-enabled AI applications and democ…