2 papers
cs.LG2026
FUSE: Fairness-aware Update Screening against Byzantine Exploitation in -Fair Federated Learning
Tiantong Sun, Jinlin Liu, Xingru Wei +4
Client-level fair federated learning, in the form of -fair federated learning (-FFL), agnostic federated learning, or tilted empirical risk minimization, has emerged as an at…
cs.DC2025
FREESH: Fair, Resource- and Energy-Efficient Scheduling for LLM Serving on Heterogeneous GPUs
Xuan He, Zequan Fang, Jinzhao Lian +3
The ever-increasing computation and energy demand for LLM and AI agents call for holistic and efficient optimization of LLM serving systems. In practice, heterogeneous GPU clusters…