research
∙
10/05/2021
Secure Aggregation for Buffered Asynchronous Federated Learning
Federated learning (FL) typically relies on synchronous training, which ...
share
research
∙
09/20/2021
ApproxIFER: A Model-Agnostic Approach to Resilient and Robust Prediction Serving Systems
Due to the surge of cloud-assisted AI services, the problem of designing...
research
∙
01/27/2021
List-Decodable Coded Computing: Breaking the Adversarial Toleration Barrier
We consider the problem of coded computing where a computational task is...
research
∙
11/11/2020
On Polynomial Approximations for Privacy-Preserving and Verifiable ReLU Networks
Outsourcing neural network inference tasks to an untrusted cloud raises ...
research
∙
11/03/2020
A Scalable Approach for Privacy-Preserving Collaborative Machine Learning
We consider a collaborative learning scenario in which multiple data-own...
research
∙
09/30/2020
Secure Aggregation with Heterogeneous Quantization in Federated Learning
Secure model aggregation across many users is a key component of federat...
research
∙
08/19/2020
Analog Lagrange Coded Computing
A distributed computing scenario is considered, where the computational ...
research
∙
07/21/2020
Byzantine-Resilient Secure Federated Learning
Secure federated learning is a privacy-preserving framework to improve m...
research
∙
07/17/2020
Privacy-Preserving Distributed Learning in the Analog Domain
We consider the critical problem of distributed learning over data while...
research
∙
07/07/2020
Coded Computing for Federated Learning at the Edge
Federated Learning (FL) is an exciting new paradigm that enables trainin...
research
∙
06/16/2020
Minimax Lower Bounds for Transfer Learning with Linear and One-hidden Layer Neural Networks
Transfer learning has emerged as a powerful technique for improving the ...
research
∙
02/11/2020
Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learning
Federated learning is gaining significant interests as it enables model ...
research
∙
01/23/2020
Coded Computing for Boolean Functions
The growing size of modern datasets necessitates a massive computation i...
research
∙
01/15/2020
Entangled Polynomial Codes for Secure, Private, and Batch Distributed Matrix Multiplication: Breaking the ”Cubic” Barrier
In distributed matrix multiplication, a common scenario is to assign eac...
research
∙
12/19/2019
Edge Computing in the Dark: Leveraging Contextual-Combinatorial Bandit and Coded Computing
With recent advancements in edge computing capabilities, there has been ...
research
∙
04/30/2019
Harmonic Coding: An Optimal Linear Code for Privacy-Preserving Gradient-Type Computation
We consider the problem of distributedly computing a general class of fu...
research
∙
04/11/2019
Timely-Throughput Optimal Coded Computing over Cloud Networks
In modern distributed computing systems, unpredictable and unreliable in...
research
∙
02/02/2019
CodedPrivateML: A Fast and Privacy-Preserving Framework for Distributed Machine Learning
How to train a machine learning model while keeping the data private and...
research
∙
01/19/2019
Fitting ReLUs via SGD and Quantized SGD
In this paper we focus on the problem of finding the optimal weights of ...
research
∙
01/10/2019
INTERPOL: Information Theoretically Verifiable Polynomial Evaluation
We study the problem of verifiable polynomial evaluation in the user-ser...
research
∙
06/04/2018
Lagrange Coded Computing: Optimal Design for Resiliency, Security and Privacy
We consider a distributed computing scenario that involves computations ...
research
∙
05/24/2018
Polynomially Coded Regression: Optimal Straggler Mitigation via Data Encoding
We consider the problem of training a least-squares regression model on ...
research
∙
05/05/2018
Compressed Coded Distributed Computing
Communication overhead is one of the major performance bottlenecks in la...
research
∙
04/17/2018
Communication-Aware Scheduling of Serial Tasks for Dispersed Computing
There is a growing interest in development of in-network dispersed compu...
research
∙
03/31/2018
Fundamental Resource Trade-offs for Encoded Distributed Optimization
Dealing with the shear size and complexity of today's massive data sets ...
research
∙
01/23/2018
Straggler Mitigation in Distributed Matrix Multiplication: Fundamental Limits and Optimal Coding
We consider the problem of massive matrix multiplication, which underlie...
research
∙
10/27/2017
Near-Optimal Straggler Mitigation for Distributed Gradient Methods
Modern learning algorithms use gradient descent updates to train inferen...
research
∙
10/17/2017
