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Clustered Federated Learning (CFL) is an emerging paradigm to extract insights from data on IoT devices. Through iterative client clustering and model aggregation, CFL adeptly manages data heterogeneity, ensures privacy, and delivers personalized models ...

Graph-based methods have witnessed tremendous success in traffic prediction, largely attributed to their superior ability in capturing and modeling spatial dependencies. However, urban-scale traffic data are usually distributed among various owners, ...

Data federation has emerged as a novel database system enabling collaborative queries across mutually distrusted data owners. Federated equi-join, a commonly used operation in data federation, combines relations from distinct data owners while preserving ...

In Internet of Things (IoT), data sharing among different devices can improve manufacture efficiency and reduce workload, and yet make the network systems be more vulnerable to various intrusion attacks. There has been realistic demand to develop ...

Federated learning is a promising learning paradigm that allows collaborative training of models across multiple data owners without sharing their raw datasets. To enhance privacy in federated learning, multi-party computation can be leveraged for ...

Ride hailing has become prevailing. Central in ride hailing platforms is taxi order dispatching which involves recommending a suitable driver for each order. Previous works use pure combinatorial optimization solutions for taxi dispatching, which suffer ...

MAUP (modifiable areal unit problem) is a fundamental problem for spatial data management and analysis. As an instantiation of MAUP in online transportation platforms, region generation (i.e., specifying the areal unit for service operations) is the ...

Personalized federated learning collaboratively trains client-specific models, which holds potential for various mobile and IoT applications with heterogeneous data. However, existing solutions are vulnerable to distribution shifts between training and ...

Similarity search is getting increasingly useful in real applications. This paper focuses on the in-memory similarity search, i.e., the range query and k nearest neighbor (kNN) query, under arbitrary metric spaces, where the only known information is the ...

Approximate nearest neighbor query is a fundamental spatial query widely applied in many real-world applications. In the big data era, there is an increasing demand to scale these queries over a spatial data federation, which consists of multiple ...

Data collected by IoT devices are often private and have a large diversity across users. Therefore, learning requires pre-training a model with available representative data samples, deploying the pre-trained model on IoT devices, and adapting the ...

Learning based order dispatching has witnessed tremendous success in ride hailing. However, the success halts within individual ride hailing platforms because sharing raw order dispatching data across platforms may leak user privacy and business secrets. ...

The increasing concerns on data security limit the sharing of data distributedly stored at multiple data owners and impede the scale of spatial queries over big urban data. In response, data federation systems have emerged to perform secure queries ...

Hierarchically Separated Tree (HST) is the most popular solution to embed a metric space into a tree metric. By using HSTs, many optimization problems, which are hard on defined metrics, become easier to get good approximation bounds with respect to the ...