2 citations · 2 across the 1 of their papers we have counts for
2 papers
cs.LG2025
FeDABoost: Fairness Aware Federated Learning with Adaptive Boosting
Tharuka Kasthuri Arachchige, Veselka Boeva, Shahrooz Abghari
This work focuses on improving the performance and fairness of Federated Learning (FL) in non IID settings by enhancing model aggregation and boosting the training of underperformi…
cs.SE2023★ 2 cited
Scaling Down to Scale Up: A Cost-Benefit Analysis of Replacing OpenAI's LLM with Open Source SLMs in Production
Chandra Irugalbandara, Ashish Mahendra, Roland Daynauth +6
Many companies use large language models (LLMs) offered as a service, like OpenAI's GPT-4, to create AI-enabled product experiences. Along with the benefits of ease-of-use and shor…