3 papers
cs.LG2026
Fed-GAME: Personalized Federated Learning with Graph Attention Mixture-of-Experts For Time-Series Forecasting
Yi Li, Han Liu, Mingfeng Fan +3
Federated learning (FL) on graphs shows promise for distributed time-series forecasting. Yet, existing methods rely on static topologies and struggle with client heterogeneity. We…
cs.CV2026
Specializing Foundation Models via Mixture of Low-Rank Experts for Comprehensive Head CT Analysis
Youngjin Yoo, Han Liu, Bogdan Georgescu +14
Foundation models pre-trained on large-scale datasets demonstrate strong transfer learning capabilities; however, their adaptation to complex multi-label diagnostic tasks-such as c…
cs.DC2025
EcoLoRA: Communication-Efficient Federated Fine-Tuning of Large Language Models
Han Liu, Ruoyao Wen, Srijith Nair +6
To address data locality and privacy restrictions, Federated Learning (FL) has recently been adopted to fine-tune large language models (LLMs), enabling improved performance on var…