In federated learning (FL), decentralized model training allows multi-ple participants to collaboratively improve a shared machine learning model without exchanging raw data. However, ensuring the integrity and reliability of the system is challenging due to the presence of potentially malicious or faulty nodes that can degrade the model's performance. This paper proposes a novel trust-based incentive mechanism designed to evaluate and reward the quality of contributions in FL systems. By dynamically assessing trust scores based on fac-tors such as data quality, model accuracy, consistency, and contribution fre-quency, the system encourages honest participation and penalizes unreliable or malicious behavior. These trust scores form the basis of an incentive mechanism that rewards high-trust nodes with greater participation opportunities and penal-ties for low-trust participants. We further explore the integration of blockchain technology and smart contracts to automate the trust evaluation and incentive distribution processes, ensuring transparency and decentralization. Our proposed theoretical framework aims to create a more robust, fair, and transparent FL eco-system, reducing the risks posed by untrustworthy participants.
D2M: A Decentralized, Privacy-Preserving, Incentive-Compatible Data Marketplace for Collaborative Learning
Department of Computer Science and Engineering, Indian Institute of Technology, Jodhpur（计算机科学与工程系，印度理工学院，乔浦尔）；School of Mathematics and Computational Sciences, Eastern University Pennsylvania（数学与计算科学学院，东部大学宾夕法尼亚）
Trustless Federated Learning at Edge-Scale: A Compositional Architecture for Decentralized, Verifiable, and Incentive-Aligned Coordination
Marshall School of Business University of Southern California（南加州大学马歇尔商学院）；School of Natural and Computing Sciences University of Aberdeen（阿伯丁大学自然与计算科学学院）
TrustChain: A Blockchain Framework for Auditing and Verifying Aggregators in Decentralized Federated Learning
Department of Electrical and Computer Engineering, University of Windsor（威省大学电气与计算机工程系）；Faculty of Computer Science, University of New Brunswick（新不伦瑞克大学计算机科学学院）
Decentralized Deep Learning for Multi-Access Edge Computing: A Survey on Communication Efficiency and Trustworthiness
University of Tokyo（东京大学）
更广泛的覆盖范围和更好的解决方案，以减少5G的延迟，需要它与多路访问边缘计算（MEC）技术相结合。分散式深度学习（Decentralized Deep Learning，简称DAI），如联邦学习（Federated Learning）和群学习（Swarm Learning），作为数百万智能边缘设备的隐私保护数据处理的一种有前途的解决方案，利用了本地客户端网络内多层神经网络的分布式计算，而不会泄露原始的本地训练数据。值得注意的是，在金融和医疗保健等行业，交易和个人医疗记录的敏感数据被谨慎地保存着，ESTA可以促进这些机构之间的合作，以提高训练模型的性能，同时保护参与客户的数据隐私。在这篇调查论文中，我们展示了分布式学习的技术基础，这些技术基础通过分散式学习使社会各界受益。此外，我们还从通信效率和可信度的新角度概述了物联网的挑战和最相关的解决方案，从而全面概述了该领域的当前最新技术水平。
Evidential Trust-Aware Model Personalization in Decentralized Federated Learning for Wearable IoT
Quantum Cloud Computing and Distributed Systems (qCLOUDS) Lab（量子云计算与分布式系统实验室）；School of Computing and Information Systems（计算与信息系统学院）；The University of Melbourne, Australia（墨尔本大学）
School of Mathematics and Statistics, Beijing Jiaotong University（北京交通大学数学与统计学院）；Department of Electrical and Electronic Engineering, Faculty of Engineering, Imperial College London（帝国理工学院工程学院电气与电子工程系）
Altruistic Ride Sharing: A Framework for Fair and Sustainable Urban Mobility via Peer-to-Peer Incentives
Dept. of CSIS and APPCAIR, BITS Pilani K K Birla Goa Campus（BITS Pilani K K Birla Goa校区计算机科学与信息系统系及应用人工智能中心）；School of Computer Science, Carnegie Mellon University（卡内基梅隆大学计算机科学学院）
TDC-Cache: A Trustworthy Decentralized Cooperative Caching Framework for Web3.0
School of Electronic and Optical Engineering, Nanjing University of Science and Technology（南京理工大学电子与光学工程学院）；College of Electronics and Information Engineering, Shenzhen University（深圳大学电子与信息工程学院）；National Mobile Communications Research Laboratory, Southeast University（东南大学国家移动通信研究中心）；Purple Mountain Laboratories（紫金山实验室）；School of Electrical Engineering and Telecommunications, The University of New South Wales（新南威尔士大学电子与电信工程学院）
National University of Singapore（新加坡国立大学）；Tsinghua University（清华大学）；Shanghai Qi Zhi Institute（上海期智研究院）；HKUST (GZ)（香港科技大学（广州））；New York University（纽约大学）；Yau Mathematical Sciences Center（丘成桐数学科学中心）
