GradualDiff-Fed transmits model deltas in federated LLM fine-tuning, but its update rule reduces exactly to FedAvg and claims significant communication savings without measuring them.
Testing the Robustness of a BiLSTM-based Structural Story Classifier
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abstract
The growing prevalence of counterfeit stories on the internet has fostered significant interest towards fast and scalable detection of fake news in the machine learning community. While several machine learning techniques for this purpose have emerged, we observe that there is a need to evaluate the impact of noise on these techniques' performance, where noise constitutes news articles being mistakenly labeled as fake (or real). This work takes a step in that direction, where we examine the impact of noise on a state-of-the-art, structural model based on BiLSTM (Bidirectional Long-Short Term Model) for fake news detection, Hierarchical Discourse-level Structure for Fake News Detection by Karimi and Tang (Reference no. 9).
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GradualDiff-Fed: A Federated Learning Specialized Framework for Large Language Model
GradualDiff-Fed transmits model deltas in federated LLM fine-tuning, but its update rule reduces exactly to FedAvg and claims significant communication savings without measuring them.