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cs.CV2025
Auditing Significance, Metric Choice, and Demographic Fairness in Medical AI Challenges
Ariel Lubonja, Pedro R. A. S. Bassi, Wenxuan Li +4
Open challenges have become the de facto standard for comparative ranking of medical AI methods. Despite their importance, medical AI leaderboards exhibit three persistent limitati…
cs.DC2025
On Harnessing Idle Compute at the Edge for Foundation Model Training
Leyang Xue, Meghana Madhyastha, Myungjin Lee +3
The foundation-model ecosystem remains highly centralized because training requires immense compute resources and is therefore largely limited to large cloud operators. Edge-assist…
cs.LG2025
Towards Decentralized and Sustainable Foundation Model Training with the Edge
Leyang Xue, Meghana Madhyastha, Randal Burns +2
Foundation models are at the forefront of AI research, appealing for their ability to learn from vast datasets and cater to diverse tasks. Yet, their significant computational dema…