论文标题
关心而无需共享：一个联合学习人群框架，用于多元化的城市代表
Caring Without Sharing: A Federated Learning Crowdsensing Framework for Diversifying Representation of Cities
论文作者
论文摘要
移动众包已成为研究人员从大规模收集公民的行为数据的主流范式。可以利用这些有价值的数据来创建集中存储库，这些存储库可用于培训高级人工智能（AI）模型，以供各个方面受益的各种服务。尽管数十年的研究已经探索了移动人群的生存能力，而诱因和许多尝试降低了参与障碍的尝试，但关于共享个人数据的隐私问题仍然存在。最近，已经出现了一条新的途径，使能够将MCS范式转向更加隐私的协作学习，即联合学习。在本文中，我们为这个新兴范式提出了一个同类框架。我们通过案例研究来证明我们的框架的功能，该案例研究通过使两种视力算法多样化，以了解普通人行道障碍的表示，这是增强视力障碍导航的一部分。
Mobile Crowdsensing has become main stream paradigm for researchers to collect behavioral data from citizens in large scales. This valuable data can be leveraged to create centralized repositories that can be used to train advanced Artificial Intelligent (AI) models for various services that benefit society in all aspects. Although decades of research has explored the viability of Mobile Crowdsensing in terms of incentives and many attempts have been made to reduce the participation barriers, the overshadowing privacy concerns regarding sharing personal data still remain. Recently a new pathway has emerged to enable to shift MCS paradigm towards a more privacy-preserving collaborative learning, namely Federated Learning. In this paper, we posit a first of its kind framework for this emerging paradigm. We demonstrate the functionalities of our framework through a case study of diversifying two vision algorithms through to learn the representation of ordinary sidewalk obstacles as part of enhancing visually impaired navigation.
