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学者姓名：杨震
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Abstract ：
Personalized Federated Learning (PFL) aims to train machine learning models on decentralized, heterogeneous data while preserving user privacy. This research survey examines the core communication challenges in PFL and evaluates optimization strategies to address key issues, including data heterogeneity, high communication costs, model drift, privacy vulnerabilities, and device variability. We provide a comprehensive analysis of key communication optimization techniques; Model Compression, Differential Privacy, Client Selection, Asynchronous Updates, Gradient Compression, and Model Caching, by their efficiency and effectiveness under diverse PFL conditions. Our study quantitatively compares these methods, identifies limitations, and proposes enhanced strategies to improve communication efficiency, reduce latency, and maintain model accuracy. This research delivers actionable insights for optimizing PFL communication, enhancing both model performance and privacy safeguards. Overall, this work serves as a valuable resource for researchers and practitioners, offering practical guidance on leveraging advanced communication techniques to drive PFL improvements and highlighting promising directions for future research.
Keyword ：
Personalized federated learning Personalized federated learning Federated learning Federated learning Communication efficiency Communication efficiency Privacy preservation Privacy preservation Communication optimization Communication optimization
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| GB/T 7714 | Sabah, Fahad , Chen, Yuwen , Yang, Zhen et al. Communication optimization techniques in Personalized Federated Learning: Applications, challenges and future directions [J]. | INFORMATION FUSION , 2025 , 117 . |
| MLA | Sabah, Fahad et al. "Communication optimization techniques in Personalized Federated Learning: Applications, challenges and future directions" . | INFORMATION FUSION 117 (2025) . |
| APA | Sabah, Fahad , Chen, Yuwen , Yang, Zhen , Raheem, Abdul , Azam, Muhammad , Ahmad, Nadeem et al. Communication optimization techniques in Personalized Federated Learning: Applications, challenges and future directions . | INFORMATION FUSION , 2025 , 117 . |
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Abstract ：
Blockchain technology, known for its decentralized and immutable nature, serves as the foundation for various applications. As a prominent application of blockchain, decentralized storage is powered by blockchain technology and is expected to provide a reliable and cost-effective alternative to traditional centralized storage. A major challenge in blockchain-powered decentralized storage is how to guarantee the quality of storage services in decentralized storage nodes (DSNs). Storage auditing can ensure the integrity and security of the stored data. Unfortunately, it incurs additional computational costs for data owners and extra storage overheads for DSNs, which thereby cannot be directly applied to decentralized storage networks consisting of nodes with various computation and storage capacity. In this article, we overcome these problems and minimize additional burdens in storage auditing. We propose EDCOMA, a computation and storage efficient auditing scheme for blockchain-based decentralized storage, in which a double compression method is designed to compress data authenticators using both data and polynomial commitment. To prevent replay attacks on double compression launched by DSNs, we introduce zero knowledge proof and design a compression arithmetic circuit to guarantee the execution of compression operations in DSNs. We analyze the security of EDCOMA under the random oracle model and conduct extensive experiments to evaluate the performance of EDCOMA. Experimental results affirm that EDCOMA outperforms state-of-the-art approaches in both computational and storage efficiency.
Keyword ：
auditing auditing Cloud computing Cloud computing Data integrity Data integrity Computational efficiency Computational efficiency Costs Costs Smart contracts Smart contracts double compression double compression Peer-to-peer computing Peer-to-peer computing blockchain blockchain Decentralized storage Decentralized storage Blockchains Blockchains
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| GB/T 7714 | Yu, Haiyang , Chen, Yurun , Yang, Zhen et al. EDCOMA: Enabling Efficient Double Compressed Auditing for Blockchain-Based Decentralized Storage [J]. | IEEE TRANSACTIONS ON SERVICES COMPUTING , 2024 , 17 (5) : 2273-2286 . |
| MLA | Yu, Haiyang et al. "EDCOMA: Enabling Efficient Double Compressed Auditing for Blockchain-Based Decentralized Storage" . | IEEE TRANSACTIONS ON SERVICES COMPUTING 17 . 5 (2024) : 2273-2286 . |
| APA | Yu, Haiyang , Chen, Yurun , Yang, Zhen , Chen, Yuwen , Yu, Shui . EDCOMA: Enabling Efficient Double Compressed Auditing for Blockchain-Based Decentralized Storage . | IEEE TRANSACTIONS ON SERVICES COMPUTING , 2024 , 17 (5) , 2273-2286 . |
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Abstract ：
Brain tumors, characterized by uncontrollable cellular growths, are a significant global health challenge. Navigating the complexities of tumor identification due to their varied dimensions and positions, our research introduces enhanced methods for precise detection. Utilizing advanced learning techniques, we've improved early identification by preprocessing clinical dataset-derived images, augmenting them via a Generative Adversarial Network, and applying an Improved Privacy-Preserving Collaborative Convolutional Neural Network (IPC-CNN) for segmentation. Recognizing the critical importance of data security in today's digital era, our framework emphasizes the preservation of patient privacy. We evaluated the performance of our proposed model on the Figshare and BRATS 2018 datasets. By facilitating a collaborative model training environment across multiple healthcare institutions, we harness the power of distributed computing to securely aggregate model updates, ensuring individual data protection while leveraging collective expertise. Our IPC-CNN model achieved an accuracy of 99.40%, marking a notable advancement in brain tumor classification and offering invaluable insights for both the medical imaging and machine learning communities.
Keyword ：
Segmentation Segmentation Brain tumors Brain tumors Privacy-Preserving Privacy-Preserving Improved Convolutional Neural Network Improved Convolutional Neural Network Generative Adversarial Network Generative Adversarial Network Figshare Figshare BRATS BRATS
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| GB/T 7714 | Raheem, Abdul , Yang, Zhen , Yu, Haiyang et al. IPC-CNN: A Robust Solution for Precise Brain Tumor Segmentation Using Improved Privacy-Preserving Collaborative Convolutional Neural Network [J]. | KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS , 2024 , 18 (9) : 2589-2604 . |
| MLA | Raheem, Abdul et al. "IPC-CNN: A Robust Solution for Precise Brain Tumor Segmentation Using Improved Privacy-Preserving Collaborative Convolutional Neural Network" . | KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS 18 . 9 (2024) : 2589-2604 . |
| APA | Raheem, Abdul , Yang, Zhen , Yu, Haiyang , Yaqub, Muhammad , Sabah, Fahad , Ahmed, Shahzad et al. IPC-CNN: A Robust Solution for Precise Brain Tumor Segmentation Using Improved Privacy-Preserving Collaborative Convolutional Neural Network . | KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS , 2024 , 18 (9) , 2589-2604 . |
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Abstract ：
Partial label learning (PLL) aims to learn a robust multi-class classifier from the ambiguous data, where each instance is given with several candidate labels, among which only one label is real. Most existing methods usually cope with such problem by utilizing a feature similarity graph to conduct label disambiguation. However, these methods construct the feature graph by only employing original features, while the influences of latent outliers and the contributions of label space are regrettably ignored. To tackle these issues, in this article, we propose a Prior KnOwledge ConsTrained Adaptive Graph FramEwork (POTAGE) for partial label learning, which utilizes an adaptive graph fused with label information to accurately describe the instance relationship and guide the desired model training. Compared with the feature-induced fixed graph, the adaptive graph is deemed to be more robust and accurate to reveal the intrinsic manifold structure within the data, and the embedding label information is expected to effectively alleviate the label ambiguities and enlarge the gap of label confidences between two instances from different classes. Extensive experiments demonstrate that POTAGE achieves state-of-the-art performance.
Keyword ：
adaptive graph adaptive graph Partial label learning Partial label learning label information embedding label information embedding label disambiguation label disambiguation
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| GB/T 7714 | Lyu, Gengyu , Feng, Songhe , Wang, Shaokai et al. Prior Knowledge Constrained Adaptive Graph Framework for Partial Label Learning [J]. | ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY , 2023 , 14 (2) . |
| MLA | Lyu, Gengyu et al. "Prior Knowledge Constrained Adaptive Graph Framework for Partial Label Learning" . | ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY 14 . 2 (2023) . |
| APA | Lyu, Gengyu , Feng, Songhe , Wang, Shaokai , Yang, Zhen . Prior Knowledge Constrained Adaptive Graph Framework for Partial Label Learning . | ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY , 2023 , 14 (2) . |
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Abstract ：
Teaching requirements elicitation to students who do not have practical experience is challenging, as they usually cannot understand the difficulty. Several recent studies have reported their experience of teaching requirements elicitation with a serious game. However, in these games, the fictitious characters have not been carefully designed to reflect real scenarios. For example, they always respond the same no matter how many times a learner interacts with them. Moreover, most existing serious games contain only one specific scenario and cannot be easily extended to cover various cases. In this paper, we design and implement a dynamic state-based serious game (BARA) for teaching requirements elicitation, which can realistically simulate real-world scenarios and automatically record learners' actions for assessment. Specifically, we model fictitious characters' behaviors using finite-state machines in order to precisely characterize the dynamic states of stakeholders. We also developed an easy-to-use editor for non-programmers to design fictitious characters and thus construct various simulated scenarios. Finally, BARA records learners' actions during the game, based on which we can gain an in-depth understanding of learners' performance and our teaching effectiveness. We evaluated BARA with 60 participants using a simulated scenario. The result shows that most participants are immersed in BARA and can reasonably complete the requirements elicitation task within the simulated scenario.
Keyword ：
serious game serious game requirements elicitation requirements elicitation empirical study empirical study goal-based design goal-based design
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| GB/T 7714 | Liu, Yu , Li, Tong , Huang, Zheqing et al. BARA: A Dynamic State-based Serious Game for Teaching Requirements Elicitation [J]. | ACM 45TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING-SOFTWARE ENGINEERING EDUCATION AND TRAINING, ICSE-SEET , 2023 : 141-152 . |
| MLA | Liu, Yu et al. "BARA: A Dynamic State-based Serious Game for Teaching Requirements Elicitation" . | ACM 45TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING-SOFTWARE ENGINEERING EDUCATION AND TRAINING, ICSE-SEET (2023) : 141-152 . |
| APA | Liu, Yu , Li, Tong , Huang, Zheqing , Yang, Zhen . BARA: A Dynamic State-based Serious Game for Teaching Requirements Elicitation . | ACM 45TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING-SOFTWARE ENGINEERING EDUCATION AND TRAINING, ICSE-SEET , 2023 , 141-152 . |
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Abstract ：
由于网络安全领域课程—网络安全与防护课程本身理论性较强，同时教学过程中缺乏对学生学习结果的有效评价方式，实现网络安全与防护课程教学目标存在一定的难度。针对网络安全与防护课程教学建设问题，本文结合线上线下多种形式，提出了新的教学设计思路，并在其中融入了教学思政元素，阐述了课程混合式教学建设过程中的课程目标、教学内容、评价方法等方面的设计。通过对课程实践结果和教学数据的分析，网络安全与防护课程混合式教学设计取得了较好的教学效果。
Keyword ：
混合式教学 混合式教学 教学实践 教学实践 网络安全与防护 网络安全与防护 教学设计 教学设计
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| GB/T 7714 | 于海阳 , 杨震 , 赖英旭 et al. 网络安全与防护课程教学设计探索 [J]. | 中国多媒体与网络教学学报(上旬刊) , 2023 , (08) : 77-80 . |
| MLA | 于海阳 et al. "网络安全与防护课程教学设计探索" . | 中国多媒体与网络教学学报(上旬刊) 08 (2023) : 77-80 . |
| APA | 于海阳 , 杨震 , 赖英旭 , 刘静 , 王一鹏 . 网络安全与防护课程教学设计探索 . | 中国多媒体与网络教学学报(上旬刊) , 2023 , (08) , 77-80 . |
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Abstract ：
The rapid development of Industrial IoT (IIoT) opens up promising possibilities for data analysis and machine learning in IIoT networks. As a distributed paradigm, federated learning (FL) allows numerous IIoT devices to collaboratively train a global model without collecting their local data together in central servers. Unfortunately, a centralized server used to aggregate local gradients can be compromised and forge the result, which incurs the need for aggregation verification. Several approaches focusing on verifying the correctness of aggregation have been proposed. However, it is still an open problem since devices have to devote more computation resources for verification, which are especially not friendly to resource-constrained IIoT devices. Furthermore, verifying weighted aggregation has not been supported in existing approaches. In this paper, we propose an efficient verifiable federated learning approach for IIoT networks, which verifies the aggregation of gradients and requires lowest burden on IIoT devices by introducing zero-knowledge proof techniques. Moreover, our design supports weighted aggregation verification to validate the aggregation of weighted gradients in the cloud server. By comparing the proposed approach with the state-of-the-art schemes including VerifyNet and VeriFL, we demonstrate the superior performance of our approach for resource-constrained devices, which minimizes the computational overheads of the IIoT devices.
Keyword ：
weighted aggregation weighted aggregation Federated learning Federated learning zero-knowledge proof zero-knowledge proof industrial IoT networks industrial IoT networks verifiability verifiability
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| GB/T 7714 | Yu, Haiyang , Xu, Runtong , Zhang, Hui et al. EV-FL: Efficient Verifiable Federated Learning With Weighted Aggregation for Industrial IoT Networks [J]. | IEEE-ACM TRANSACTIONS ON NETWORKING , 2023 , 32 (2) : 1723-1737 . |
| MLA | Yu, Haiyang et al. "EV-FL: Efficient Verifiable Federated Learning With Weighted Aggregation for Industrial IoT Networks" . | IEEE-ACM TRANSACTIONS ON NETWORKING 32 . 2 (2023) : 1723-1737 . |
| APA | Yu, Haiyang , Xu, Runtong , Zhang, Hui , Yang, Zhen , Liu, Huan . EV-FL: Efficient Verifiable Federated Learning With Weighted Aggregation for Industrial IoT Networks . | IEEE-ACM TRANSACTIONS ON NETWORKING , 2023 , 32 (2) , 1723-1737 . |
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Abstract ：
Decentralized federated learning tries to address the single point of failure and privacy issue of federated learning by leveraging committee-based blockchain, which has been extensively studied among academic and industrial fields. The introduction of committees improves the efficiency of decentralized federated learning. However, it also is prone to attacks from Byzantine committee members, which interfere with the correctness of the global model by modifying aggregation results. Therefore, the security of committees is the key challenge for decentralized federated learning via committee-based blockchain. To solve this problem, in this paper, we propose VDFChain, a secure and verifiable decentralized federated learning scheme via committee-based blockchain. Specifically, based on the polynomial commitment technique, we propose a trusted committee mechanism, which can defend against attacks from Byzantine committee members and ensure the correctness of the aggregation model. Moreover, we use lossless masking techniques and committee mechanisms to effectively provide secure aggregation. For Byzantine attacks in decentralized federated learning, different from traditional defense methods against it, the VDFChain improves the fault tolerance of decentralized federated learning and provides a feasible and practical solution to build a secure decentralized federated learning. Security analysis shows that our scheme is provably secure. We have conducted extensive comparison experiments to evaluate the performance of the proposed framework, and experimental results show that our scheme has superior computational and communication performance compared to the state-of-the-art schemes.
Keyword ：
Decentralized federated learning Decentralized federated learning Polynomial commitment techniques Polynomial commitment techniques Committee-based blockchain Committee-based blockchain Byzantine attacks Byzantine attacks Masking techniques Masking techniques Secure aggregation Secure aggregation
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| GB/T 7714 | Zhou, Ming , Yang, Zhen , Yu, Haiyang et al. VDFChain: Secure and verifiable decentralized federated learning via committee-based blockchain [J]. | JOURNAL OF NETWORK AND COMPUTER APPLICATIONS , 2023 , 223 . |
| MLA | Zhou, Ming et al. "VDFChain: Secure and verifiable decentralized federated learning via committee-based blockchain" . | JOURNAL OF NETWORK AND COMPUTER APPLICATIONS 223 (2023) . |
| APA | Zhou, Ming , Yang, Zhen , Yu, Haiyang , Yu, Shui . VDFChain: Secure and verifiable decentralized federated learning via committee-based blockchain . | JOURNAL OF NETWORK AND COMPUTER APPLICATIONS , 2023 , 223 . |
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Abstract ：
Personalized federated learning (PFL) is an exciting approach that allows machine learning (ML) models to be trained on diverse and decentralized sources of data, while maintaining client privacy and autonomy. However, PFL faces several challenges that can deteriorate the performance and effectiveness of the learning process. These challenges include data heterogeneity, communication overhead, model privacy, model drift, client heterogeneity, label noise and imbalance, federated optimization challenges, and client participation and engagement. To address these challenges, researchers are exploring innovative techniques and algorithms that can enable efficient and effective PFL. These techniques include several optimization algorithms. This research survey provides an overview of the challenges and motivations related to the model optimization strategies for PFL, as well as the state-of-the-art (SOTA) methods and algorithms which seek to provide solutions of these challenges. Overall, this survey can be a valuable resource for researchers who are interested in the emerging field of PFL as well as its potential for personalized machine learning in a federated environment.
Keyword ：
Distributed machine learning Distributed machine learning Personalized federated learning Personalized federated learning Model optimization Model optimization Collaborative learning Collaborative learning Privacy-preserving Privacy-preserving
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| GB/T 7714 | Sabah, Fahad , Chen, Yuwen , Yang, Zhen et al. Model optimization techniques in personalized federated learning: A survey [J]. | EXPERT SYSTEMS WITH APPLICATIONS , 2023 , 243 . |
| MLA | Sabah, Fahad et al. "Model optimization techniques in personalized federated learning: A survey" . | EXPERT SYSTEMS WITH APPLICATIONS 243 (2023) . |
| APA | Sabah, Fahad , Chen, Yuwen , Yang, Zhen , Azam, Muhammad , Ahmad, Nadeem , Sarwar, Raheem . Model optimization techniques in personalized federated learning: A survey . | EXPERT SYSTEMS WITH APPLICATIONS , 2023 , 243 . |
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Abstract ：
Bayesian personalized ranking (BPR) has been proposed as an effective method to model pairwise learning, and it is widely used in many personalized recommender systems. However, the effectiveness of BPR can be seriously affected by an imbalanced data distribution because it tends to rank popular items ahead of personalized items. As a result, the personalized needs of users cannot be well met. In this paper, we propose a novel personalized recommendation method called similarity pairwise ranking (SPR) to rank users' favorite items first. SPR eliminates the differences in the scores between popular and personalized items based on their similarity by using a new penalty. In such a way, the SPR-enhanced recommendation will render meaningful and personalized results that better meet the individual needs of users, and it overcomes the negative impact of imbalanced datasets. We design a model to illustrate the improvement of SPR: similarity pairwise ranking matrix factorization (SPRMF). Experimental results obtained using six datasets indicate the superiority in recommendation quality of SPRMF over the recent state-of-the-art methods. (C) 2021 Elsevier B.V. All rights reserved.
Keyword ：
Recommender system Recommender system Similar item pair Similar item pair Matrix factorization Matrix factorization Pairwise method Pairwise method
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| GB/T 7714 | Liu, Junrui , Yang, Zhen , Li, Tong et al. SPR: Similarity pairwise ranking for personalized recommendation [J]. | KNOWLEDGE-BASED SYSTEMS , 2022 , 239 . |
| MLA | Liu, Junrui et al. "SPR: Similarity pairwise ranking for personalized recommendation" . | KNOWLEDGE-BASED SYSTEMS 239 (2022) . |
| APA | Liu, Junrui , Yang, Zhen , Li, Tong , Wu, Di , Wang, Ruiyi . SPR: Similarity pairwise ranking for personalized recommendation . | KNOWLEDGE-BASED SYSTEMS , 2022 , 239 . |
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