14 citations · 63 across the 27 of their papers we have counts for
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cs.DC2025
HetRL: Efficient Reinforcement Learning for LLMs in Heterogeneous Environments
Yongjun He, Shuai Zhang, Jiading Gai +5
As large language models (LLMs) continue to scale and new GPUs are released even more frequently, there is an increasing demand for LLM post-training in heterogeneous environments…
cs.DC2020
FedAT: A High-Performance and Communication-Efficient Federated Learning System with Asynchronous Tiers
Zheng Chai, Yujing Chen, Ali Anwar +3
Federated learning (FL) involves training a model over massive distributed devices, while keeping the training data localized. This form of collaborative learning exposes new trade…
cs.DC2019
Asynchronous Online Federated Learning for Edge Devices with Non-IID Data
Yujing Chen, Yue Ning, Martin Slawski +1
Federated learning (FL) is a machine learning paradigm where a shared central model is learned across distributed edge devices while the training data remains on these devices. Fed…