Analog lagrange coded computing
A distributed computing scenario is considered, where the computational power of a set of
worker nodes is used to perform a certain computation task over a dataset that is dispersed …
worker nodes is used to perform a certain computation task over a dataset that is dispersed …
Coded distributed computing with partial recovery
Coded computation techniques provide robustness against straggling workers in distributed
computing. However, most of the existing schemes require exact provisioning of the …
computing. However, most of the existing schemes require exact provisioning of the …
List-decodable coded computing: Breaking the adversarial toleration barrier
We consider the problem of coded computing, where a computational task is performed in a
distributed fashion in the presence of adversarial workers. We propose techniques to break …
distributed fashion in the presence of adversarial workers. We propose techniques to break …
Adaptive private distributed matrix multiplication
We consider the problem of designing codes with flexible rate (referred to as rateless
codes), for private distributed matrix-matrix multiplication. A master server owns two private …
codes), for private distributed matrix-matrix multiplication. A master server owns two private …
Secure private and adaptive matrix multiplication beyond the singleton bound
We consider the problem of designing secure and private codes for distributed matrix-matrix
multiplication. A master server owns two private matrices and hires worker nodes to help …
multiplication. A master server owns two private matrices and hires worker nodes to help …
Analog secret sharing with applications to private distributed learning
We consider the critical problems of distributed computing and learning over data while
kee** it private from the computational servers. The state-of-the-art approaches to this …
kee** it private from the computational servers. The state-of-the-art approaches to this …
Distributed matrix-vector multiplication with sparsity and privacy guarantees
We consider the problem of designing a coding scheme that allows both sparsity and
privacy for distributed matrix-vector multiplication. Perfect information-theoretic privacy …
privacy for distributed matrix-vector multiplication. Perfect information-theoretic privacy …
Privacy-preserving distributed learning in the analog domain
M Soleymani, H Mahdavifar, AS Avestimehr - ar** it private
from the computational servers. The state-of-the-art approaches to this problem rely on …
from the computational servers. The state-of-the-art approaches to this problem rely on …
Gradient coding with dynamic clustering for straggler-tolerant distributed learning
Distributed implementations are crucial in speeding up large scale machine learning
applications. Distributed gradient descent (GD) is widely employed to parallelize the …
applications. Distributed gradient descent (GD) is widely employed to parallelize the …
Coded computing via binary linear codes: Designs and performance limits
We consider the problem of coded distributed computing where a large linear computational
job, such as a matrix multiplication, is divided into smaller tasks, encoded using an linear …
job, such as a matrix multiplication, is divided into smaller tasks, encoded using an linear …