88 citations · 239 across the 32 of their papers we have counts for
32 papers
Analysis and Optimization of Wireless Multimodal Federated Learning on Modal Heterogeneity
Xuefeng Han, Wen Chen, Jun Li +6
Multimodal federated learning (MFL) is a distributed framework for training multimodal models without uploading local multimodal data of clients, thereby effectively protecting cli…
Resource Efficient Asynchronous Federated Learning for Digital Twin Empowered IoT Network
Shunfeng Chu, Jun Li, Jianxin Wang +4
As an emerging technology, digital twin (DT) can provide real-time status and dynamic topology mapping for Internet of Things (IoT) devices. However, DT and its implementation with…
Blockchain-aided wireless federated learning: Resource allocation and client scheduling
Jun Li, Weiwei Zhang, Kang Wei +4
Federated learning (FL) based on the centralized design faces both challenges regarding the trust issue and a single point of failure. To alleviate these issues, blockchain-aided d…
Trustworthy DNN Partition for Blockchain-enabled Digital Twin in Wireless IIoT Networks
Xiumei Deng, Jun Li, Long Shi +5
Digital twin (DT) has emerged as a promising solution to enhance manufacturing efficiency in industrial Internet of Things (IIoT) networks. To promote the efficiency and trustworth…
EmInspector: Combating Backdoor Attacks in Federated Self-Supervised Learning Through Embedding Inspection
Yuwen Qian, Shuchi Wu, Kang Wei +5
Federated self-supervised learning (FSSL) has recently emerged as a promising paradigm that enables the exploitation of clients' vast amounts of unlabeled data while preserving dat…
Robust Model Aggregation for Heterogeneous Federated Learning: Analysis and Optimizations
Yumeng Shao, Jun Li, Long Shi +6
Conventional synchronous federated learning (SFL) frameworks suffer from performance degradation in heterogeneous systems due to imbalanced local data size and diverse computing po…
Deploying Graph Neural Networks in Wireless Networks: A Link Stability Viewpoint
Jun Li, Weiwei Zhang, Kang Wei +3
As an emerging artificial intelligence technology, graph neural networks (GNNs) have exhibited promising performance across a wide range of graph-related applications. However, inf…
Towards Communication-efficient Federated Learning via Sparse and Aligned Adaptive Optimization
Xiumei Deng, Jun Li, Kang Wei +6
Adaptive moment estimation (Adam), as a Stochastic Gradient Descent (SGD) variant, has gained widespread popularity in federated learning (FL) due to its fast convergence. However,…
Energy-Efficient Wireless Federated Learning via Doubly Adaptive Quantization
Xuefeng Han, Wen Chen, Jun Li +5
Federated learning (FL) has been recognized as a viable distributed learning paradigm for training a machine learning model across distributed clients without uploading raw data. H…
Refine, Discriminate and Align: Stealing Encoders via Sample-Wise Prototypes and Multi-Relational Extraction
Shuchi Wu, Chuan Ma, Kang Wei +4
This paper introduces RDA, a pioneering approach designed to address two primary deficiencies prevalent in previous endeavors aiming at stealing pre-trained encoders: (1) suboptima…
Federated Meta-Learning for Few-Shot Fault Diagnosis with Representation Encoding
Jixuan Cui, Jun Li, Zhen Mei +5
Deep learning-based fault diagnosis (FD) approaches require a large amount of training data, which are difficult to obtain since they are located across different entities. Federat…
Analysis and Optimization of Wireless Federated Learning with Data Heterogeneity
Xuefeng Han, Jun Li, Wen Chen +4
With the rapid proliferation of smart mobile devices, federated learning (FL) has been widely considered for application in wireless networks for distributed model training. Howeve…
Efficient Federated Learning with Enhanced Privacy via Lottery Ticket Pruning in Edge Computing
Yifan Shi, Kang Wei, Li Shen +4
Federated learning (FL) is a collaborative learning paradigm for decentralized private data from mobile terminals (MTs). However, it suffers from issues in terms of communication,…
Towards the Flatter Landscape and Better Generalization in Federated Learning under Client-level Differential Privacy
Yifan Shi, Kang Wei, Li Shen +4
To defend the inference attacks and mitigate the sensitive information leakages in Federated Learning (FL), client-level Differentially Private FL (DPFL) is the de-facto standard f…
Gradient Sparsification for Efficient Wireless Federated Learning with Differential Privacy
Kang Wei, Jun Li, Chuan Ma +5
Federated learning (FL) enables distributed clients to collaboratively train a machine learning model without sharing raw data with each other. However, it suffers the leakage of p…
Design of Two-Level Incentive Mechanisms for Hierarchical Federated Learning
Shunfeng Chu, Jun Li, Kang Wei +4
Hierarchical Federated Learning (HFL) is a distributed machine learning paradigm tailored for multi-tiered computation architectures, which supports massive access of devices' mode…
Amplitude-Varying Perturbation for Balancing Privacy and Utility in Federated Learning
Xin Yuan, Wei Ni, Ming Ding +3
While preserving the privacy of federated learning (FL), differential privacy (DP) inevitably degrades the utility (i.e., accuracy) of FL due to model perturbations caused by DP no…
Make Landscape Flatter in Differentially Private Federated Learning
Yifan Shi, Yingqi Liu, Kang Wei +3
To defend the inference attacks and mitigate the sensitive information leakages in Federated Learning (FL), client-level Differentially Private FL (DPFL) is the de-facto standard f…
Improving the Model Consistency of Decentralized Federated Learning
Yifan Shi, Li Shen, Kang Wei +4
To mitigate the privacy leakages and communication burdens of Federated Learning (FL), decentralized FL (DFL) discards the central server and each client only communicates with its…
Low-latency Federated Learning with DNN Partition in Distributed Industrial IoT Networks
Xiumei Deng, Jun Li, Chuan Ma +4
Federated Learning (FL) empowers Industrial Internet of Things (IIoT) with distributed intelligence of industrial automation thanks to its capability of distributed machine learnin…
Trusted AI in Multi-agent Systems: An Overview of Privacy and Security for Distributed Learning
Chuan Ma, Jun Li, Kang Wei +5
Motivated by the advancing computational capacity of distributed end-user equipments (UEs), as well as the increasing concerns about sharing private data, there has been considerab…
Vertical Federated Learning: Challenges, Methodologies and Experiments
Kang Wei, Jun Li, Chuan Ma +5
Recently, federated learning (FL) has emerged as a promising distributed machine learning (ML) technology, owing to the advancing computational and sensing capacities of end-user d…
Low-Latency Federated Learning over Wireless Channels with Differential Privacy
Kang Wei, Jun Li, Chuan Ma +5
In federated learning (FL), model training is distributed over clients and local models are aggregated by a central server. The performance of uploaded models in such situations ca…
Blockchain Assisted Federated Learning over Wireless Channels: Dynamic Resource Allocation and Client Scheduling
Xiumei Deng, Jun Li, Chuan Ma +5
The blockchain technology has been extensively studied to enable distributed and tamper-proof data processing in federated learning (FL). Most existing blockchain assisted FL (BFL)…