11 citations · 32 across the 14 of their papers we have counts for
7 papers · 1 filter
Every Parameter Matters: Ensuring the Convergence of Federated Learning with Dynamic Heterogeneous Models Reduction
Hanhan Zhou, Tian Lan, Guru Venkataramani +1
Cross-device Federated Learning (FL) faces significant challenges where low-end clients that could potentially make unique contributions are excluded from training large models due…
Federated PAC-Bayesian Learning on Non-IID data
Zihao Zhao, Yang Liu, Wenbo Ding +1
Existing research has either adapted the Probably Approximately Correct (PAC) Bayesian framework for federated learning (FL) or used information-theoretic PAC-Bayesian bounds while…
Cooperative Multi-Type Multi-Agent Deep Reinforcement Learning for Resource Management in Space-Air-Ground Integrated Networks
Hengxi Zhang, Huaze Tang, Wenbo Ding +1
The Space-Air-Ground Integrated Network (SAGIN), integrating heterogeneous devices including low earth orbit (LEO) satellites, unmanned aerial vehicles (UAVs), and ground users (GU…
AQUILA: Communication Efficient Federated Learning with Adaptive Quantization in Device Selection Strategy
Zihao Zhao, Yuzhu Mao, Zhenpeng Shi +4
The widespread adoption of Federated Learning (FL), a privacy-preserving distributed learning methodology, has been impeded by the challenge of high communication overheads, typica…
PerFedRec++: Enhancing Personalized Federated Recommendation with Self-Supervised Pre-Training
Sichun Luo, Yuanzhang Xiao, Xinyi Zhang +3
Federated recommendation systems employ federated learning techniques to safeguard user privacy by transmitting model parameters instead of raw user data between user devices and t…
Stabilizing and Improving Federated Learning with Non-IID Data and Client Dropout
Jian Xu, Meiling Yang, Wenbo Ding +1
The label distribution skew induced data heterogeniety has been shown to be a significant obstacle that limits the model performance in federated learning, which is particularly de…
Optimizing Trading Strategies in Quantitative Markets using Multi-Agent Reinforcement Learning
Hengxi Zhang, Zhendong Shi, Yuanquan Hu +3
Quantitative markets are characterized by swift dynamics and abundant uncertainties, making the pursuit of profit-driven stock trading actions inherently challenging. Within this c…