2 papers
cs.CL2026
Perplexity Predicts Protection: Choosing Pretrained Backbones for Worst-Client Fairness in Federated Parameter-Efficient Fine-Tuning
Kiran Naseer, Samreen Azhar, Umar Shoaib +2
Federated learning lets multiple parties train a shared model without pooling their data, but a client with far less data than the others can end up poorly served even when the gro…
cs.CL2025
GHaLIB: A Multilingual Framework for Hope Speech Detection in Low-Resource Languages
Ahmed Abdullah, Sana Fatima, Haroon Mahmood
Hope speech has been relatively underrepresented in Natural Language Processing (NLP). Current studies are largely focused on English, which has resulted in a lack of resources for…