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The Power of Bias: Optimizing Client Selection in Federated Learning with Heterogeneous Differential Privacy
Jiating Ma;Yipeng Zhou;Qi Li;Quan Z. Sheng;Laizhong Cui;Jiangchuan Liu
IEEE Transactions on Dependable and Secure Computing
Year: 2025 | Early Access Article |
To preserve the data privacy, the federated learning (FL) paradigm emerges in which clients only expose model gradients rather than original data for conducting model training. To enhance the protection of model gradients in FL, differentially private federated learning (DPFL) is proposed which incorporates differentially private (DP) noises to obfuscate gradients before they are exposed. Yet, an ...Show More
Snapshot: Towards Application-Centered Models for Pedestrian Trajectory Prediction in Urban Traffic Environments
Nico Uhlemann;Yipeng Zhou;Tobias Simeon Mohr;Markus Lienkamp
2025 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW)
Year: 2025 | Conference Paper |
This paper explores pedestrian trajectory prediction in urban traffic while focusing on both model accuracy and real-world applicability. While promising approaches exist, they often revolve around pedestrian datasets excluding traffic-related information, or resemble architectures that are either not real-time capable or robust. To address these limitations, we first introduce a dedicated benchma...Show More
Mitigating Poisoning Attacks in Federated Learning Through Deep One-Class Classification
Anqi Zhang;Ping Zhao;Wenke Lu;Yipeng Zhou;Wenqian Zhang;Guanglin Zhang
IEEE Transactions on Cognitive Communications and Networking
Year: 2025 | Early Access Article |
Federated learning (FL) enables clients to learn a machine learning model collaboratively without sharing their private local data to the server. However, due to its distributed structure, FL is vulnerable to poisoning attacks where adversaries intentionally send the poisoned local model parameters to the server and further affect the behavior of the global model. Existing works on mitigating pois...Show More
CPFedAvg: Enhancing Hierarchical Federated Learning via Optimized Local Aggregation and Parameter Mixing
Xuezheng Liu;Yipeng Zhou;Di Wu;Miao Hu;Min Chen;Mohsen Guizani;Quan Z. Sheng
IEEE Transactions on Networking
Year: 2025 | Early Access Article |
Hierarchical federated learning (HFL) improves the scalability and efficiency of traditional federated learning (FL) by incorporating a hierarchical topology into the FL framework. In a typical HFL system, clients are divided into multiple tiers, and the training process involves both local and global model aggregation. However, existing HFL approaches have several significant drawbacks. Firstly, ...Show More
Toward Optimized Federated Learning With Compressed Communications by Rate Adaption
Laizhong Cui;Xiaoxin Su;Yipeng Zhou;Jiangchuan Liu;Shiting Wen
IEEE Transactions on Networking
Year: 2025 | Early Access Article |
It is known that federated learning (FL) incurs heavy communication overhead for model training by exchanging model updates between clients and the parameter server (PS) over the Internet for multiple rounds. Compressing model updates is an effective approach to alleviating communication overhead in FL. Yet the tradeoff between compression and model accuracy in the networked environment remains un...Show More
FCER: A Federated Cloud-Edge Recommendation Framework With Cluster-Based Edge Selection
The traditional recommendation system provides web services by modeling user behavior characteristics, which also faces the risk of leaking user privacy. To mitigate the rising concern on privacy leakage in recommender systems, federated learning (FL) based recommendation has received tremendous attention, which can preserve data privacy by conducting local model training on clients. However, devi...Show More
CRS: A Cost-Aware Resource Scheduling Framework for Deep Learning Task Orchestration in Mobile Clouds
Deep learning (DL) has found extensive application in supporting various mobile applications. The efficient execution of DL tasks is paramount for ensuring the effectiveness of AI-driven mobile applications. While previous research has predominantly focused on minimizing the completion time of DL tasks, the associated cost of execution has often been overlooked. Nonetheless, cost becomes a critica...Show More
FGLBA: Enabling Highly-Effective and Stealthy Backdoor Attack on Federated Graph Learning
Qing Lu;Miao Hu;Di Wu;Yipeng Zhou;Mohsen Guizani;Quan Z. Sheng
2024 IEEE International Conference on Data Mining (ICDM)
Year: 2024 | Conference Paper |
Federated graph learning (FGL) has risen as a promising paradigm for collaboratively training graph neural networks while safeguarding data privacy. Nevertheless, the distributed nature of FGL also renders it susceptible to backdoor attacks. Although backdoor attacks are recognized as a significant threat to both centralized graph learning and federated learning (FL), the study of such attacks in ...Show More
Adaptive Global-local Fusion Network Based Deep Unsupervised Hashing for Remote Sensing Image Retrieval
Zekun Xu;Yipeng Zhou;Quan Z. Sheng;Chao Li;Tongtong Lou;Weipeng Jing
2024 IEEE International Conference on Multimedia and Expo (ICME)
Year: 2024 | Conference Paper |
Unsupervised hashing methods have gained wide-spread popularity for remote sensing (RS) image retrieval due to their high efficiency. Existing methods heavily rely on similarity matrix generated by pre-trained models as supervised signals. However, such pre-trained models obtained from natural images fail to comprehensively extract features in RS images, yielding unreliable similarity relationship...Show More
GazeFed: Privacy-Aware Personalized Gaze Prediction for Virtual Reality
Jiang Wu;Xuezheng Liu;Miao Hu;Hongxu Lin;Min Chen;Yipeng Zhou;Di Wu
2024 IEEE/ACM 32nd International Symposium on Quality of Service (IWQoS)
Year: 2024 | Conference Paper |
Gaze prediction is essential for enhancing user experiences of virtual reality (VR) applications. However, existing methods seldom considered the privacy nature of gaze data, which may reveal both psychological and physiological characteristics of VR users. Moreover, the commonly adopted one-sizefits-all prediction model cannot well capture behavioral patterns of different VR users. In this paper,...Show More
History-Aware Privacy Budget Allocation for Model Training on Evolving Data-Sharing Platforms
Linchang Xiao;Xianzhi Zhang;Di Wu;Miao Hu;Yipeng Zhou;Shui Yu
The publicly released machine learning (ML) models are susceptible to malicious attacks (e.g., gradient leakage attacks), which may expose sensitive training data of data-sharing platforms to untrusted third-parties. To preserve the privacy of training data, differential privacy (DP) is exploited to limit the amount of leaked privacy with a predefined budget, which in fact is a non-recoverable res...Show More
Fed-CVLC: Compressing Federated Learning Communications with Variable-Length Codes
Xiaoxin Su;Yipeng Zhou;Laizhong Cui;John C. S. Lui;Jiangchuan Liu
IEEE INFOCOM 2024 - IEEE Conference on Computer Communications
Year: 2024 | Conference Paper |
In Federated Learning (FL) paradigm, a parameter server (PS) concurrently communicates with distributed participating clients for model collection, update aggregation, and model distribution over multiple rounds, without touching private data owned by individual clients. FL is appealing in preserving data privacy; yet the communication between the PS and scattered clients can be a severe bottlenec...Show More
DEthna: Accurate Ethereum Network Topology Discovery with Marked Transactions
Chonghe Zhao;Yipeng Zhou;Shengli Zhang;Taotao Wang;Quan Z. Sheng;Song Guo
IEEE INFOCOM 2024 - IEEE Conference on Computer Communications
Year: 2024 | Conference Paper |
In Ethereum, the ledger exchanges messages along an underlying Peer-to-Peer (P2P) network to reach consistency. Understanding the underlying network topology of Ethereum is crucial for network optimization, security and scalability. However, the accurate discovery of Ethereum network topology is non-trivial due to its deliberately designed security mechanism. Consequently, existing measuring schem...Show More
Expediting In-Network Federated Learning by Voting-Based Consensus Model Compression
IEEE INFOCOM 2024 - IEEE Conference on Computer Communications
Year: 2024 | Conference Paper |
Cited by: Papers (1)
Recently, federated learning (FL) has gained momentum because of its capability in preserving data privacy. To conduct model training by FL, multiple clients exchange model updates with a parameter server via Internet. To accelerate the communication speed, it has been explored to deploy a programmable switch (PS) in lieu of the parameter server to coordinate clients. The challenge to deploy the P...Show More
Fast-Convergent Wireless Federated Learning: A Voting-Based TopK Model Compression Approach
Xiaoxin Su;Yipeng Zhou;Laizhong Cui;Quan Z. Sheng;Yinggui Wang;Song Guo
Federated learning (FL) has been extensively exploited in the training of machine learning models to preserve data privacy. In particular, wireless FL enables multiple clients to collaboratively train models by sharing model updates via wireless communication without exposing raw data. The state-of-the-art wireless FL advocates efficient aggregation of model updates from multiple clients by over-t...Show More
FedDP-SA: Boosting Differentially Private Federated Learning via Local Data Set Splitting
Xuezheng Liu;Yipeng Zhou;Di Wu;Miao Hu;Jessie Hui Wang;Mohsen Guizani
Federated learning (FL) emerges as an attractive collaborative machine learning framework that enables training of models across decentralized devices by merely exposing model parameters. However, malicious attackers can still hijack communicated parameters to expose clients’ raw samples resulting in privacy leakage. To defend against such attacks, differentially private FL (DPFL) is devised, whic...Show More
Accelerating Blockchain-Enabled Federated Learning with Clustered Clients
Laizhong Cui;Yinghao Li;Yipeng Zhou;Youyang Qu;Jiangchuan Liu
IEEE Transactions on Big Data
Year: 2024 | Early Access Article |
With the rapid development of big data, Federated learning (FL) has found numerous applications, enabling machine learning (ML) on edge devices while preserving privacy. However, FL still faces crucial challenges, such as single point of failure and poisoning attacks, which motivate the integration of blockchain-enabled FL (BeFL). Beyond that, the efficiency issue still limits the further applicat...Show More
Boosting Dynamic Decentralized Federated Learning by Diversifying Model Sources
Recently, federated learning (FL) has received intensive research because of its ability in preserving data privacy for scattered clients to collaboratively train machine learning models. Decentralized federated learning (DFL) is upgraded from FL which allows clients to aggregate model parameters with their neighbours directly. DFL is particularly feasible for dynamic systems, in which the neighbo...Show More
SDSR: Optimizing Metaverse Video Streaming via Saliency-Driven Dynamic Super-Resolution
Baili Chai;Jinyu Chen;Zhenxiao Luo;Zelong Wang;Miao Hu;Yipeng Zhou;Di Wu
Metaverse (especially 360-degree) video streaming allows broadcasting virtual events in the metaverse to a broad audience. To reduce the huge bandwidth consumption, quite a few super-resolution (SR)-enhanced 360-degree video streaming systems have been proposed. However, there is very limited work to investigate how the granularity of SR model affects the system performance, and how to choose a pr...Show More
CSRA: Robust Incentive Mechanism Design for Differentially Private Federated Learning
The differentially private federated learning (DPFL) paradigm emerges to firmly preserve data privacy from two perspectives. First, decentralized clients merely exchange model updates rather than raw data with a parameter server (PS) over multiple communication rounds for model training. Secondly, model updates to be exposed to the PS will be distorted by clients with differentially private (DP) n...Show More
Exploring the Practicality of Differentially Private Federated Learning: A Local Iteration Tuning Approach
Yipeng Zhou;Runze Wang;Jiahao Liu;Di Wu;Shui Yu;Yonggang Wen
Although Federated Learning (FL) prevents the exposure of original data samples when collaboratively training machine learning models among decentralized clients, it has been revealed that vanilla FL is still susceptible to adversarial attacks if model parameters are leaked to malicious attackers. To enhance the protection level of FL, Differential Private Federated Learning (DPFL) has been propos...Show More
An Optimized Sparse Response Mechanism for Differentially Private Federated Learning
Federated Learning (FL) enables geo-distributed clients to collaboratively train a learning model without exposing their private data. By only exposing local model parameters, FL well preserves data privacy of clients. Yet, it remains possible to recover raw samples from over frequently exposed parameters resulting in privacy leakage. Differentially private federated learning (DPFL) has recently b...Show More
Live Migration of Video Analytics Applications in Edge Computing
Chenghao Rong;Jessie Hui Wang;Jilong Wang;Yipeng Zhou;Jun Zhang
In order to schedule resources efficiently or maintain applications’ continuity for mobile customers, edge platforms often need to adaptively migrate the applications on them. However, our measurement shows that existing migration solutions cannot solve the issue of migrating video analytics applications in edge computing because the memory states of video analytics applications have different cha...Show More
AutoFL: A Bayesian Game Approach for Autonomous Client Participation in Federated Edge Learning
Miao Hu;Wenzhuo Yang;Zhenxiao Luo;Xuezheng Liu;Yipeng Zhou;Xu Chen;Di Wu
Given that devices (i.e., clients) participating in federated edge learning (FEL) are autonomous and resource-constrained in nature, it is critical to design effective incentive mechanisms to encourage client participation so as to improve the performance of FEL. In this article, we aim to boost the FEL training efficiency by answering how much compute resource should clients autonomously contribu...Show More
Enhancing Federated Learning by One-Shot Transferring of Intermediate Features from Clients
Youxingzhu Deng;Yipeng Zhou;Gang Liu;Jessie Hui Wang;Yu Shui
2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA)
Year: 2023 | Conference Paper |
Federated learning (FL) is an emerging paradigm using a parameter server (PS) to coordinate multiple decentralized clients for training a common model without exposing their raw data. Despite its amazing capability in preserving data privacy, FL confronts two significant challenges that have not been sufficiently addressed by existing works, which are: 1) heterogeneous data distributed on clients ...Show More
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