Journal Article10.1109/tkde.2023.3311816
Task Assignment with Efficient Federated Preference Learning in Spatial Crowdsourcing
Hao Miao,Xiaolong Zhong,Jiaxin Liu,Yan Zhao,Xiangyu Zhao,Weizhu Qian,Kai Zheng,Christian S. Jensen +7 more
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TL;DR: Task Assignment with Efficient Federated Preference Learning in Spatial Crowdsourcing efficiently assigns tasks based on worker preferences while ensuring privacy in spatial crowdsourcing platforms.
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Abstract: Spatial Crowdsourcing (SC) is finding widespread application in today's online world. As we have transitioned from desktop crowdsourcing applications (e.g., Wikipedia) to SC applications (e.g., Uber), there is a sense that SC systems must not only provide effective task assignment but also need to ensure privacy. To achieve these often-conflicting objectives, we propose a framework, Task Assignment with Federated Preference Learning, that performs task assignment based on worker preferences while keeping the data decentralized and private in each platform center (e.g., each delivery center of an SC company). The framework includes a federated preference learning phase and a task assignment phase. Specifically, in the first phase, we build a local preference model for each platform center based on historical data. We provide means of horizontal federated learning that makes it possible to collaboratively train these local preference models under the orchestration of a central server. Specifically, we provide a practical method that accelerates federated preference learning based on stochastic controlled averaging and achieves low communication costs while considering data heterogeneity among clients. The task assignment phase aims to achieve effective and efficient task assignment by considering workers’ preferences. Extensive evaluations on real data offer insight into the effectiveness and efficiency of the paper's proposals.
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Citations
Spatial-Temporal Large Language Model for Traffic Prediction
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TL;DR: A novel partially frozen attention strategy of the LLM is proposed, which is designed to capture spatial-temporal dependencies for traffic prediction and exhibits robust performance in both few-shot and zero-shot prediction scenarios.
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HDMixer: Hierarchical Dependency with Extendable Patch for Multivariate Time Series Forecasting
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TL;DR: This paper proposes HDMixer, a pure MLP-based model for multivariate time series forecasting, addressing limitations of existing patch-based methods by introducing a Length-Extendable Patcher and Hierarchical Dependency Explorer to capture rich semantic information and hierarchical interactions.
Spatial-Temporal Large Language Model for Traffic Prediction
Chenxi Liu,Yang Sun,Qianxiong Xu,Zhishuai Li,Long Cheng,Ziyue Li,Rui Zhao +6 more
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Federated Adaptation for Foundation Model-based Recommendations
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TL;DR: Federated adaptation for foundation model-based recommendations enhances recommendation systems by enabling the foundation model to capture user preference changes in a privacy-preserving manner.
Sample Quality Heterogeneity-aware Federated Causal Discovery through Adaptive Variable Space Selection
Xianjie Guo,Kui Yu,Hao Wang,Lizhen Cui,Han Yu,Xiaoxiao Li +5 more
- 01 Aug 2024
TL;DR: This paper proposes the Federated Adaptive Causal Discovery (FedACD), a method that adaptively selects the causal relationships learned under the "good" variable space from each client, while masking those learned under the "bad" variable space during federated model aggregation.
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Geo-indistinguishability: differential privacy for location-based systems
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