11 citations · 32 across the 14 of their papers we have counts for
14 papers
FL-TAC: Enhanced Fine-Tuning in Federated Learning via Low-Rank, Task-Specific Adapter Clustering
Siqi Ping, Yuzhu Mao, Yang Liu +2
Although large-scale pre-trained models hold great potential for adapting to downstream tasks through fine-tuning, the performance of such fine-tuned models is often limited by the…
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…
WS-3D-Lane: Weakly Supervised 3D Lane Detection With 2D Lane Labels
Jianyong Ai, Wenbo Ding, Jiuhua Zhao +1
Compared to 2D lanes, real 3D lane data is difficult to collect accurately. In this paper, we propose a novel method for training 3D lanes with only 2D lane labels, called weakly s…
Towards Efficient Communications in Federated Learning: A Contemporary Survey
Zihao Zhao, Yuzhu Mao, Yang Liu +4
In the traditional distributed machine learning scenario, the user's private data is transmitted between clients and a central server, which results in significant potential privac…
FedHAP: Federated Hashing with Global Prototypes for Cross-silo Retrieval
Meilin Yang, Jian Xu, Yang Liu +1
Deep hashing has been widely applied in large-scale data retrieval due to its superior retrieval efficiency and low storage cost. However, data are often scattered in data silos wi…
Deep Leakage from Model in Federated Learning
Zihao Zhao, Mengen Luo, Wenbo Ding
Distributed machine learning has been widely used in recent years to tackle the large and complex dataset problem. Therewith, the security of distributed learning has also drawn in…
On the Convergence of Heterogeneous Federated Learning with Arbitrary Adaptive Online Model Pruning
Hanhan Zhou, Tian Lan, Guru Venkataramani +1
One of the biggest challenges in Federated Learning (FL) is that client devices often have drastically different computation and communication resources for local updates. To this…
Communication Efficient Federated Learning with Adaptive Quantization
Yuzhu Mao, Zihao Zhao, Guangfeng Yan +4
Federated learning (FL) has attracted tremendous attentions in recent years due to its privacy preserving measures and great potentials in some distributed but privacy-sensitive ap…