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2,975 papers
Modern Cyber-Physical Systems (CPS) demand high computational capability, strong security guarantees, and energy-aware operation, particularly in embedded, mobile, and autonomous platforms such as robotics, intelligent vehicles, and industrial controllers. Addressing these requirements calls for heterogeneous System-on...
With the rapid advancement and deep integration of the Internet of Things (IoT) and 5G technologies, mobile edge computing (MEC) has undertaken an increasingly important role in enhancing service quality. Leveraging their high mobility and flexible deployment, unmannedaerial vehicles (UAVs) extend MEC services to chall...
Many real-world problems involve hierarchical multi-objective optimization over coupled sequential decisions. A common strategy is to combine heuristic planning with deep reinforcement learning (DRL). While existing hybrid methods often show strong empirical performance, the coupled closed-loop learning dynamics betwee...
Hierarchical federated learning (HFL) emerges as a promising solution for distributed systems in real-world IoT, compensating for the limitations of client and training data through multilevel aggregation. However, severe system heterogeneity, such as data heterogeneity, differentiated communication and computing power...
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Artificial Intelligence-Generated Content is reshaping interactive content creation. However, provisioning diffusion-based interactive denoising services in mobile edge networks remains challenging, due to the demanding computation and communication resources. To address these challenges, in this paper, we investigate...
The exponential growth of edge devices and growing demand for low-latency, high-throughput applications have established edge computing as essential infrastructure. Edge computing enables near-data computation to alleviate device burdens, yet task offloading optimization remains challenging due to the heterogeneous, dy...
Container-based microservices provide a lightweight and scalable foundation for edge computing, where layer sharing improves storage and throughput efficiency. However, the unpredictable nature of edge networks leads to variable layer download times and thus bandwidth uncertainty. This article addresses the problem of...
As edge computing nodes and platform-class devices such as industrial gateways become increasingly prevalent, the x86-64 architecture is widely used in these deployments, making memory-safety protection against vulnerabilities in x86-64 commercial off-the-shelf (COTS) binaries a critical security requirement. However,...
Uncrewed aerial vehicle (UAV)-enabled mobile edge computing (MEC) has emerged as a promising paradigm for future wireless communication systems by providing flexible and efficient computation offloading services to resource-limited ground user devices (UDs). However, the nonuniform user distribution and mobility, heter...
The Federated Multi-Armed Bandit (FMAB) framework is proposed to facilitate collaborative model training in cloud-edge environments. Most existing FMAB-based systems assume that participants have personal datasets. This assumption becomes biased in scenarios with limited local data or when the discrepancy between histo...
Offloading DNN workload to an edge network for parallel and distributed computing is a promising solution to hosting computation-intensive DNN services. The challenge is how to partition DNN models and to reduce the communication to realize a high-performance multi-edge DNN computing. In this paper, we first propose a...
Edge devices handle sensitive user data, raising significant security and privacy concerns when shared with data collectors for downstream learning tasks. One potential solution leverages differentially private generative models, keeping original data on user devices and creating obfuscated variants for transmission. H...
Host Sowbhagya Suresh shares some of the latest news and cutting-edge research from across Microsoft—from understanding the challenges facing coding agents to uncovering how people use AI at work and improving extended-range weather forecasting. The post Microsoft Research Focus – Edition #97 appeared first on Microsoft Research.
Professor Paris Smaragdis develops computers with superhuman hearing for applications like speech recognition, music understanding, and environmental monitoring.
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For decades, a fundamental question about the difficulty of computing in high-dimensional spaces has resisted proof. New research resolves this question, removing a major theoretical obstacle to understanding the efficiency of randomized algorithms used across science and computing. The Kannan–Lovász–Simonovits (KLS) conjecture was proposed in 1995. It asks whether, for a broad class of high-dimensional […] The post Solving a 31-year-old grand challenge in mathematics – the KLS conjecture appear…
A new computational approach reveals how cells communicate to assemble into tissues. This could accelerate stem cell engineering, improve disease models, and inform therapies.
Zero-knowledge proofs can verify computing results without rerunning workloads or exposing sensitive data. Magnetar helps bring them into the mainstream with support for standard programming languages, major speed gains, and quantum-resistant security. The post Proof-based general-purpose computing with Magnetar appeared first on Microsoft Research.
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