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cs.DC2025
Deadline-Aware Online Scheduling for LLM Fine-Tuning with Spot Market Predictions
Linggao Kong, Yuedong Xu, Lei Jiao +1
As foundation models grow in size, fine-tuning them becomes increasingly expensive. While GPU spot instances offer a low-cost alternative to on-demand resources, their volatile pri…
cs.LG2025
Reconciling Communication Compression and Byzantine-Robustness in Distributed Learning
Diksha Gupta, Antonio Honsell, Chuan Xu +2
Distributed learning enables scalable model training over decentralized data, but remains hindered by Byzantine faults and high communication costs. While both challenges have been…
cs.LG2025
Cutting Through Privacy: A Hyperplane-Based Data Reconstruction Attack in Federated Learning
Francesco Diana, André Nusser, Chuan Xu +1
Federated Learning (FL) enables collaborative training of machine learning models across distributed clients without sharing raw data, ostensibly preserving data privacy. Neverthel…