Saurav Prakash
Saurav Prakash
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Machine Unlearning of Federated Clusters
Federated clustering (FC) is an unsupervised learning problem that arises in a number of practical applications, including personalized …
Chao Pan
,
Jin Sima
,
Saurav Prakash
,
Vishal Rana
,
Olgica Milenkovic
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Hierarchical coded gradient aggregation for learning at the edge
Client devices at the edge are generating increasingly large amounts of rich data suitable for learning powerful statistical models. …
Saurav Prakash
,
Amirhossein Reisizadeh
,
Ramtin Pedarsani
,
Salman Avestimehr
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DOI
pSConv: A pre-defined sparse kernel based convolution for deep CNNs
The high demand for computational and storage resources severely impedes the deployment of deep convolutional neural networks (CNNs) in …
Souvik Kundu
,
Saurav Prakash
,
Haleh Akrami
,
Peter Beerel
,
Keith Chugg
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DOI
Tree gradient coding
Scaling up distributed machine learning systems face two major bottlenecks – delays due to stragglers and limited communication …
Amirhossein Reisizadeh
,
Saurav Prakash
,
Ramtin Pedarsani
,
Salman Avestimehr
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DOI
Coded computing for distributed graph analytics
Many distributed graph computing systems have been developed recently for efficient processing of massive graphs. These systems …
Saurav Prakash
,
Amirhossein Reisizadeh
,
Ramtin Pedarsani
,
Salman Avestimehr
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DOI
Coded computation over heterogeneous clusters
In large-scale distributed computing clusters, such as Amazon EC2, there are several types of ‘system noise’ that can …
Amirhossein Reisizadeh
,
Saurav Prakash
,
Ramtin Pedarsani
,
Salman Avestimehr
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