2 citations · 5 across the 5 of their papers we have counts for
12 papers
Stream Iterative Distributed Coded Computing for Learning Applications in Heterogeneous Systems
Homa Esfahanizadeh, Alejandro Cohen, Muriel Medard
To improve the utility of learning applications and render machine learning solutions feasible for complex applications, a substantial amount of heavy computations is needed. Thus,…
Syfer: Neural Obfuscation for Private Data Release
Adam Yala, Victor Quach, Homa Esfahanizadeh +5
Balancing privacy and predictive utility remains a central challenge for machine learning in healthcare. In this paper, we develop Syfer, a neural obfuscation method to protect aga…
NeuraCrypt: Hiding Private Health Data via Random Neural Networks for Public Training
Adam Yala, Homa Esfahanizadeh, Rafael G. L. D' Oliveira +6
Balancing the needs of data privacy and predictive utility is a central challenge for machine learning in healthcare. In particular, privacy concerns have led to a dearth of public…
Stream Distributed Coded Computing
Alejandro Cohen, Guillaume Thiran, Homa Esfahanizadeh +1
The emerging large-scale and data-hungry algorithms require the computations to be delegated from a central server to several worker nodes. One major challenge in the distributed c…
Bringing Network Coding into SDN: A Case-study for Highly Meshed Heterogeneous Communications
Alejandro Cohen, Homa Esfahanizadeh, Bruno Sousa +6
Modern communications have moved away from point-to-point models to increasingly heterogeneous network models. In this article, we propose a novel controller-based protocol to depl…
Spatially Coupled Codes with Sub-Block Locality: Joint Finite Length-Asymptotic Design Approach
Homa Esfahanizadeh, Eshed Ram, Yuval Cassuto +1
SC-LDPC codes with sub-block locality can be decoded locally at the level of sub-blocks that are much smaller than the full code block, thus providing fast access to the coded info…