65 citations · 111 across the 15 of their papers we have counts for
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cs.CV2023
Navigating Data Heterogeneity in Federated Learning A Semi-Supervised Federated Object Detection
Taehyeon Kim, Eric Lin, Junu Lee +2
Federated Learning (FL) has emerged as a potent framework for training models across distributed data sources while maintaining data privacy. Nevertheless, it faces challenges with…
cs.LG2023★ 8 cited
Does fine-tuning GPT-3 with the OpenAI API leak personally-identifiable information?
Albert Yu Sun, Eliott Zemour, Arushi Saxena +4
Machine learning practitioners often fine-tune generative pre-trained models like GPT-3 to improve model performance at specific tasks. Previous works, however, suggest that fine-t…