Federated Learning (FL) is an emerging direction in distributed machine learning (ML) that
enables in-situ model training and testing on edge data. Despite having the same end goals …

We present FedScale, a federated learning (FL) benchmarking suite with realistic datasets
and a scalable runtime to enable reproducible FL research. FedScale datasets encompass …

The proliferation of camera-enabled devices and large video repositories has led to a
diverse set of video analytics applications. These applications rely on video pipelines …

Serverless computing has seen rapid growth due to the ease-of-use and cost-efficiency it
provides. However, function scheduling, a critical component of serverless systems, has …

The Internet of Things (IoT) is on the verge of a major paradigm shift. In the IoT system of the
future, IoFT, the “cloud” will be substituted by the “crowd” where model training is brought to …

With growing deployment of machine learning (ML) models, ML developers are training or re-
training increasingly more deep neural networks (DNNs). They do so to find the most …

Federated learning (FL) aims to train machine learning (ML) models across potentially
millions of edge client devices. Yet, training and customizing models for FL clients is …

Serverless computing has seen rapid growth due to the ease-of-use and cost-efficiency it
provides. However, function scheduling, a critical component of serverless systems, has …

Federated Learning (FL) is an emerging distributed machine learning (ML) technique that
enables in-situ model training and inference on decentralized edge devices. We propose …

Federated learning (FL) is an emerging machine learning (ML) paradigm that enables
heterogeneous edge devices to collaboratively train ML models without revealing their raw …