38 citations · 39 across the 5 of their papers we have counts for
5 papers
PRISM: Predictive Runtime In-place Scaling and Model Selection for Edge Microservices
Uwe Gropengießer, Thomas Reuter, Dominik Schön +3
Latency-sensitive edge AI services must balance strict deadlines, output quality, and limited compute and energy budgets. However, static CPU provisioning wastes resources because…
Accelerated Training on Low-Power Edge Devices
Mohamed Aboelenien Ahmed, Kilian Pfeiffer, Heba Khdr +3
Training on edge devices poses several challenges as these devices are generally resource-constrained, especially in terms of power. State-of-the-art techniques at the device level…
Apodotiko: Enabling Efficient Serverless Federated Learning in Heterogeneous Environments
Mohak Chadha, Alexander Jensen, Jianfeng Gu +2
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…
Training Heterogeneous Client Models using Knowledge Distillation in Serverless Federated Learning
Mohak Chadha, Pulkit Khera, Jianfeng Gu +2
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…
GreenCourier: Carbon-Aware Scheduling for Serverless Functions
Mohak Chadha, Thandayuthapani Subramanian, Eishi Arima +3
This paper presents GreenCourier, a novel scheduling framework that enables the runtime scheduling of serverless functions across geographically distributed regions based on their…