3 papers
cs.LG2025
FedEve: On Bridging the Client Drift and Period Drift for Cross-device Federated Learning
Tao Shen, Zexi Li, Didi Zhu +3
Federated learning (FL) is a machine learning paradigm that allows multiple clients to collaboratively train a shared model without exposing their private data. Data heterogeneity…
cs.LG2025
Each Rank Could be an Expert: Single-Ranked Mixture of Experts LoRA for Multi-Task Learning
Ziyu Zhao, Yixiao Zhou, Zhi Zhang +10
Low-Rank Adaptation (LoRA) is widely used for adapting large language models (LLMs) to specific domains due to its efficiency and modularity. Meanwhile, vanilla LoRA struggles with…
cs.LG2024
FedCFA: Alleviating Simpson's Paradox in Model Aggregation with Counterfactual Federated Learning
Zhonghua Jiang, Jimin Xu, Shengyu Zhang +5
Federated learning (FL) is a promising technology for data privacy and distributed optimization, but it suffers from data imbalance and heterogeneity among clients. Existing FL met…