2 papers
cs.LG2024
C2A: Client-Customized Adaptation for Parameter-Efficient Federated Learning
Yeachan Kim, Junho Kim, Wing-Lam Mok +2
Despite the versatility of pre-trained language models (PLMs) across domains, their large memory footprints pose significant challenges in federated learning (FL), where the traini…
cs.CL2023
Improving Bias Mitigation through Bias Experts in Natural Language Understanding
Eojin Jeon, Mingyu Lee, Juhyeong Park +3
Biases in the dataset often enable the model to achieve high performance on in-distribution data, while poorly performing on out-of-distribution data. To mitigate the detrimental e…