25 citations · 29 across the 4 of their papers we have counts for
4 papers
Overcoming Environmental Meta-Stationarity in MARL via Adaptive Curriculum and Counterfactual Group Advantage
Weiqiang Jin, Yang Liu, Shixiang Tang +5
Multi-agent reinforcement learning (MARL) has reached competitive performance on cooperative tasks against scripted adversaries, yet most methods train agents at a single fixed dif…
FedFixer: Mitigating Heterogeneous Label Noise in Federated Learning
Xinyuan Ji, Zhaowei Zhu, Wei Xi +4
Federated Learning (FL) heavily depends on label quality for its performance. However, the label distribution among individual clients is always both noisy and heterogeneous. The h…
AdapterFL: Adaptive Heterogeneous Federated Learning for Resource-constrained Mobile Computing Systems
Ruixuan Liu, Ming Hu, Zeke Xia +5
Federated Learning (FL) enables collaborative learning of large-scale distributed clients without data sharing. However, due to the disparity of computing resources among massive m…
AdaptiveFL: Adaptive Heterogeneous Federated Learning for Resource-Constrained AIoT Systems
Chentao Jia, Ming Hu, Zekai Chen +4
Although Federated Learning (FL) is promising to enable collaborative learning among Artificial Intelligence of Things (AIoT) devices, it suffers from the problem of low classifica…