EUROSYS

Federated Learning (FL) provides a privacy-preserving mechanism for distributed training of machine learning models on networked devices (e.g., mobile devices, IoT edge nodes). It enables Artificial Intelligence (AI) at the edge by creating models ...

Blockchains today amass terabytes of transaction data that demand efficient and insightful real-time analytics for applications such as smart contract hack detection, price arbitrage on decentralized exchanges, or trending token analysis. Conventional ...

The pub/sub paradigm facilitates communication among heterogeneous edge and IoT devices for distributed edge applications. At the same time, the increasing number of devices and sensors at the edge leads to higher network congestion, which requires more ...

WebAssembly (Wasm) has been attracting attention as a common platform for edge computing thanks to its architecture neutrality, sandboxing for security, and lightweight characteristics that fit the requirements for distributed and cooperated processing ...

Cerberus uses face detection in edge devices to perform privacy-preserving crowd counting and localisation. We describe its deployment in a university setting where ceiling-mounted cameras perform real-time face detection to report occupied seats without ...

Inference latency prediction on mobile devices is essential for multiple applications, including collaborative inference and neural architecture search. Training accurate latency predictors using ML techniques requires sufficient and representative data; ...

Stream processing is becoming increasingly significant in various scenarios, including security-sensitive sectors. It benefits from keeping data in memory, which exposes large volumes of data in use, thereby emphasising the need for protection. The ...

GPUs are emerging as the most popular accelerator for many applications, powering the core of machine learning applications. In networked GPU-accelerated applications input & output data typically traverse the CPU and the OS network stack multiple times, ...

Minimizing database access latency is crucial in serverless edge computing for many applications, but databases are predominantly deployed in cloud environments, resulting in costly network round-trips. Embedding an in-process database library such as ...

As IoT devices multiply and produce vast volumes of data, there is a heightened demand for instantaneous data processing. However, traditional cloud computing cannot adequately address these demands due to its latency and bandwidth limitations. Edge ...