855 citations · 3.9k across the 142 of their papers we have counts for
13 papers · 1 filter
Coded Computing for Federated Learning at the Edge
Saurav Prakash, Sagar Dhakal, Mustafa Akdeniz +2
Federated Learning (FL) is an exciting new paradigm that enables training a global model from data generated locally at the client nodes, without moving client data to a centralize…
Coded Computing for Secure Boolean Computations
Chien-Sheng Yang, A. Salman Avestimehr
The growing size of modern datasets necessitates splitting a large scale computation into smaller computations and operate in a distributed manner. Adversaries in a distributed sys…
Jupiter: A Networked Computing Architecture
Pradipta Ghosh, Quynh Nguyen, Pranav K Sakulkar +7
In the era of Internet of Things, there is an increasing demand for networked computing to support the requirements of the time-constrained, compute-intensive distributed applicati…
Edge Computing in the Dark: Leveraging Contextual-Combinatorial Bandit and Coded Computing
Chien-Sheng Yang, Ramtin Pedarsani, A. Salman Avestimehr
With recent advancements in edge computing capabilities, there has been a significant increase in utilizing the edge cloud for event-driven and time-sensitive computations. However…
Train Where the Data is: A Case for Bandwidth Efficient Coded Training
Zhifeng Lin, Krishna Giri Narra, Mingchao Yu +2
Training a machine learning model is both compute and data-intensive. Most of the model training is performed on high performance compute nodes and the training data is stored near…
Collage Inference: Achieving low tail latency during distributed image classification using coded redundancy models
Krishna Narra, Zhifeng Lin, Ganesh Ananthanarayanan +2
Reducing the latency variance in machine learning inference is a key requirement in many applications. Variance is harder to control in a cloud deployment in the presence of stragg…