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Found 12 papers, 3 papers with code
NeRCC: Nested-Regression Coded Computing for Resilient Distributed Prediction Serving Systems
Resilience against stragglers is a critical element of prediction serving systems, tasked with executing inferences on input data for a pre-trained machine-learning model.
ByzSecAgg: A Byzantine-Resistant Secure Aggregation Scheme for Federated Learning Based on Coded Computing and Vector Commitment
In this paper, we propose ByzSecAgg, an efficient secure aggregation scheme for federated learning that is protected against Byzantine attacks and privacy leakages.
SwiftAgg+: Achieving Asymptotically Optimal Communication Loads in Secure Aggregation for Federated Learning
We propose SwiftAgg+, a novel secure aggregation protocol for federated learning systems, where a central server aggregates local models of $N \in \mathbb{N}$ distributed users, each of size $L \in \mathbb{N}$, trained on their local data, in a privacy-preserving manner.
SwiftAgg: Communication-Efficient and Dropout-Resistant Secure Aggregation for Federated Learning with Worst-Case Security Guarantees
We propose SwiftAgg, a novel secure aggregation protocol for federated learning systems, where a central server aggregates local models of $N$ distributed users, each of size $L$, trained on their local data, in a privacy-preserving manner.
Optimal Communication-Computation Trade-Off in Heterogeneous Gradient Coding
Gradient coding allows a master node to derive the aggregate of the partial gradients, calculated by some worker nodes over the local data sets, with minimum communication cost, and in the presence of stragglers.
Berrut Approximated Coded Computing: Straggler Resistance Beyond Polynomial Computing
In this technique, coding is used across data sets, and computation is done over coded data, such that the results of an arbitrary subset of worker nodes with a certain size are enough to recover the final results.
DRL-Based QoS-Aware Resource Allocation Scheme for Coexistence of Licensed and Unlicensed Users in LTE and Beyond
In this paper, we employ deep reinforcement learning to develop a novel radio resource allocation and packet scheduling scheme for different Quality of Service (QoS) requirements applicable to LTEadvanced and 5G networks.
A Hybrid-Order Distributed SGD Method for Non-Convex Optimization to Balance Communication Overhead, Computational Complexity, and Convergence Rate
In this paper, we propose a method of distributed stochastic gradient descent (SGD), with low communication load and computational complexity, and still fast convergence.
Corella: A Private Multi Server Learning Approach based on Correlated Queries
One of the major challenges in this setup is to guarantee the privacy of the client data.
Coded Fourier Transform
We consider the problem of computing the Fourier transform of high-dimensional vectors, distributedly over a cluster of machines consisting of a master node and multiple worker nodes, where the worker nodes can only store and process a fraction of the inputs.
Polynomial Codes: an Optimal Design for High-Dimensional Coded Matrix Multiplication
We consider a large-scale matrix multiplication problem where the computation is carried out using a distributed system with a master node and multiple worker nodes, where each worker can store parts of the input matrices.
Information Theory Distributed, Parallel, and Cluster Computing Information Theory
Coded TeraSort
We focus on sorting, which is the building block of many machine learning algorithms, and propose a novel distributed sorting algorithm, named Coded TeraSort, which substantially improves the execution time of the TeraSort benchmark in Hadoop MapReduce.
Distributed, Parallel, and Cluster Computing Information Theory Information Theory
