Convolutional neural networks (CNNs) are becoming more and more important for solving
challenging and critical problems in many fields. CNN inference applications have been …

Deep neural networks (DNNs) have achieved remarkable success in many fields. However,
large-scale DNNs also bring storage costs when storing snapshots for preventing clusters' …

Top-k sparsification method is popular and powerful forreducing the communication cost in
Federated Learning (FL). However, according to our experimental observation, it spends …

Abstract Unmanned Aerial Vehicles (UAVs), which can operate autonomously in dynamic
and complex environments, are becoming increasingly common. Deep learning techniques …

Trigger-Action Program (TAP) is a simple but powerful format to realize intelligent IoT
applications, especially in home automation scenarios. Existing trace-driven approaches …

Deep learning has been used in various domains, including Web services. Convolutional
neural networks (CNNs), which are deep learning representatives, are among the most …

Top-K sparsification-based compression techniques are popular and powerful for reducing
communication costs in federated learning (FL). However, existing Top-K sparsification …

Deep Neural Networks (DNNs) have achieved remarkable success in various real-world
applications. However, running a Deep Neural Network (DNN) typically requires hundreds …

With the promise of federated learning (FL) to allow for geographically-distributed and highly
personalized services, the efficient exchange of model updates between clients and servers …

The likelihood of encountering in-training failures rises substantially with larger Deep
Learning (DL) training workloads, leading to lost work and resource wastage. Such failures …