Vertical federated learning (VFL) is an emerging paradigm that allows different parties (e.g., organizations or enterprises) to collaboratively build machine models with privacy protection. In the training phase, VFL only exchanges intermediate statistics, i.e., forward activations and backward derivatives, across compute model gradients. Nevertheless, due its geo-distributed nature, usually su...

Federated learning (FL) is an emerging privacy-preserving paradigm that enables multiple participants collaboratively to train a global model without uploading raw data. Considering heterogeneous computing and communication capabilities of different participants, asynchronous FL can avoid the stragglers effect in synchronous adapts scenarios with vast participants. Both staleness non-IID data w...

Mobile edge caching can effectively reduce service delay but may introduce information staleness, calling for timely content refreshing. However, refreshing consumes additional transmission resources and degrade the performance of mobile systems. In this work, we propose a freshness-aware scheme to balance freshness measured by Age Information (AoI). Specifically, cached items will be refreshed...

This paper investigates an air-ground integrated multi-access edge computing system, which is deployed by infrastructure provider (InP). Under a business agreement with the InP, third-party service provides services to subscribed mobile users (MUs). MUs compete for shared spectrum and resources over time achieve their distinctive goals. From perspective of MU, we deliberately define age update ...

Age of Information is a newly introduced metric, getting vivid attention for measuring the freshness information in real-time networks. This parameter has evolved to guarantee reception timely from latest status update, received by user any application. In this paper, we study centralized, closed-loop, networked controlled industrial wireless sensor-actuator network cyber-physical production sy...

Due to high data volumes and unpredictable arrival rates, continuous query systems processing expensive queries in real-time may fail to keep up with the input data streams resulting in buffer overflow and uncontrolled loss of data. In this work, we explore join direction adaptation (JDA) to tackle resource-limited processing of multi-join stream queries. While the existing JDA solutions alloca...

Most data stream processing systems model their inputs as append-only sequences dfg of data elements. In this model, the application expects to receive a query answer on the complete input stream. However, there are many situations in which each data element (or a window of data elements) in the stream is in fact an update to a previous one, and therefore, the most recent arrival is all that re...

Reader-writer locks (rwlocks) aim to maximize parallelism among readers, but many existing rwlocks either cause readers to contend, or significantly extend writer latency, or both. Further, some scalable rwlocks cannot cope with OS semantics like sleeping inside critical sections, preemption and conditional wait. Though truly scalable rwlocks exist, some of them cannot handle preemption, sleepi...

Federated edge learning (FEEL) emerges as a privacy-preserving paradigm to effectively train deep models from the distributed data in 6G networks. Nevertheless, limited coverage of single server results an insufficient number participating client nodes, which may impair performance. In this paper, we investigate novel FEEL framework, namely semi-decentralized federated (SD-FEEL), where multiple...