LocationTrails: a federated approach to learning location embeddings
LocationTrails: a federated approach to learning location embeddings
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DOI：
10.1145/3487351.3490964
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发表时间：
2021-11
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通讯作者：
Saket Gurukar;Srinivas Parthasarathy;R. Ramnath;Catherine Calder;Sobhan Moosavi
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作者：
Saket Gurukar;Srinivas Parthasarathy;R. Ramnath;Catherine Calder;Sobhan Moosavi
学习反映人类流动性模式的位置的矢量表示，对各种任务有用，包括位置建议，城市规划，城市分析，甚至了解邻里对个人健康和福祉的影响。现有的模拟和学习此类表示形式的方法不规模或需要大量资源进行扩展。他们通常需要将整个数据与中间数据表示形式一起加载到内存中（通常是共同位置图），并且在低资源嵌入式系统（例如边缘设备）上执行通常不可行。我们在本文中寻求解决的研究问题是，一个人可以开发有效的联合学习模型，以用于位置表示学习，从而可以在边缘设备上发生培训和随后的模型更新？我们提出了一个简单而新颖的模型，称为LocationTrails，用于学习有效的位置嵌入以解决此问题。我们表明，我们提出的模型可以在联合学习范式下进行培训，因此可以确保可以以分布式方式对模型进行培训，而无需所有用户访问的地点，从而减轻了某些隐私风险。我们评估了从两种用例中绘制的五个现实世界中的人类流动数据集上的位置性能（其中四个来自从国家保险机构获得的轨迹数据；其中一个来自对城市环境中青少年流动性模式的独特研究）。我们将我们提出的位置模型与网络表示学习领域的强基础进行了比较。我们从更好地嵌入质量生成，记忆消耗和执行时间方面显示了位置问题的功效。据我们所知，联合位置轨迹模型是第一个可以生成有效的位置嵌入的模型，而无需将完整的数据加载到中央服务器上。
Learning a vector representation of locations that reflect human mobility patterns is useful for various tasks, including location recommendation, city planning, urban analysis, and even understanding the neighborhood effects on individuals' health and well-being. Existing approaches that model and learn such representations either do not scale or require significant resources to scale. They often need the entire data to be loaded in memory along with the intermediate data representation (typically a co-location graph) and are usually not feasible to execute on low-resource embedding systems such as edge devices. The research question we seek to address in this article is, can one develop efficient federated learning models for location representation learning such that the training and the subsequent updates of the model can occur on edge devices? We present a simple yet novel model called LocationTrails for learning efficient location embeddings to address this question. We show that our proposed model can be trained under the federated learning paradigm and can, therefore, ensure that the model can be trained in a distributed fashion without centralizing locations visited by all users, thereby mitigating some risks to privacy. We evaluate the performance of LocationTrails on five real-world human mobility datasets drawn from two use cases (four of them from driving trajectory data obtained from a national insurance agency; and one of them from a unique study of adolescent mobility patterns in an urban setting). We compare our proposed LocationTrails model against the strong baselines from the network representation learning field. We show the efficacy of LocationTrails in terms of better embedding quality generation, memory consumption, and execution time. To the best of our knowledge, the federated LocationTrails model is the first model that can generate efficient location embeddings without requiring the complete data to be loaded on a central server.