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Found 8 papers, 5 papers with code
Apodotiko: Enabling Efficient Serverless Federated Learning in Heterogeneous Environments
Federated Learning (FL) is an emerging machine learning paradigm that enables the collaborative training of a shared global model across distributed clients while keeping the data decentralized.
Training Heterogeneous Client Models using Knowledge Distillation in Serverless Federated Learning
To address these challenges and enable heterogeneous client models in serverless FL, we utilize Knowledge Distillation (KD) in this paper.
FedLesScan: Mitigating Stragglers in Serverless Federated Learning
We implement our strategy by extending an open-source serverless FL system called FedLess.
IAD: Indirect Anomalous VMMs Detection in the Cloud-based Environment
A fault or an anomaly in the VMM can propagate to the VMs hosted on it and ultimately affect the availability and reliability of the applications running on those VMs.
FedLess: Secure and Scalable Federated Learning Using Serverless Computing
These include rapid scalability, no infrastructure management, automatic scaling to zero for idle clients, and a pay-per-use billing model.
DeepEdgeBench: Benchmarking Deep Neural Networks on Edge Devices
In this work, we present and compare the performance in terms of inference time and power consumption of the four Systems on a Chip (SoCs): Asus Tinker Edge R, Raspberry Pi 4, Google Coral Dev Board, Nvidia Jetson Nano, and one microcontroller: Arduino Nano 33 BLE, on different deep learning models and frameworks.
Online Memory Leak Detection in the Cloud-based Infrastructures
A memory leak in an application deployed on the cloud can affect the availability and reliability of the application.
A Systematic Mapping Study in AIOps
IT systems of today are becoming larger and more complex, rendering their human supervision more difficult.
