FedFixer: Mitigating Heterogeneous Label Noise in Federated Learning

no code implementations • 25 Mar 2024 • Xinyuan Ji, Zhaowei Zhu, Wei Xi, Olga Gadyatskaya, Zilong Song, Yong Cai, Yang Liu

The high loss incurred by client-specific samples in heterogeneous label noise poses challenges for distinguishing between client-specific and noisy label samples, impacting the effectiveness of existing label noise learning approaches.

Federated Learning

Meta Generative Flow Networks with Personalization for Task-Specific Adaptation

no code implementations • 16 Jun 2023 • Xinyuan Ji, Xu Zhang, Wei Xi, Haozhi Wang, Olga Gadyatskaya, Yinchuan Li

Multi-task reinforcement learning and meta-reinforcement learning have been developed to quickly adapt to new tasks, but they tend to focus on tasks with higher rewards and more frequent occurrences, leading to poor performance on tasks with sparse rewards.

Meta-Learning Meta Reinforcement Learning +1