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Found 10 papers, 4 papers with code
Coded Matrix Computations for D2D-enabled Linearized Federated Learning
Federated learning (FL) is a popular technique for training a global model on data distributed across client devices.
Detection and Mitigation of Byzantine Attacks in Distributed Training
For strong attacks, we demonstrate a reduction in the fraction of distorted gradients ranging from 16%-99% as compared to the prior state-of-the-art.
Aspis: Robust Detection for Distributed Learning
We prove the Byzantine resilience and detection guarantees of Aspis under weak and strong attacks and extensively evaluate the system on various large-scale training scenarios.
ByzShield: An Efficient and Robust System for Distributed Training
Our numerical experiments indicate over a 36% reduction on average in the fraction of corrupted gradients compared to the state of the art.
Federated Over-Air Subspace Tracking from Incomplete and Corrupted Data
In this work we study the problem of Subspace Tracking with missing data (ST-miss) and outliers (Robust ST-miss).
Resolvable Designs for Speeding up Distributed Computing
In this work, we show that a class of combinatorial structures called resolvable designs can be used to develop efficient coded distributed computing schemes for both the single and multiple job scenarios considered in prior work.
Random Convolutional Coding for Robust and Straggler Resilient Distributed Matrix Computation
Distributed matrix computations (matrix-vector and matrix-matrix multiplications) are at the heart of several tasks within the machine learning pipeline.
Information Theory Information Theory
CAMR: Coded Aggregated MapReduce
Many big data algorithms executed on MapReduce-like systems have a shuffle phase that often dominates the overall job execution time.
Erasure coding for distributed matrix multiplication for matrices with bounded entries
We demonstrate a tradeoff between the assumed absolute value bounds on the matrix entries and the recovery threshold.
Leveraging Coding Techniques for Speeding up Distributed Computing
Appropriate interpretation of resolvable designs can allow for the development of coded distributed computing schemes where the splitting levels are exponentially lower than prior work.
Information Theory Information Theory
