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Found 6 papers, 4 papers with code
THC: Accelerating Distributed Deep Learning Using Tensor Homomorphic Compression
To address this bottleneck and accelerate training, a widely-deployed approach is compression.
QUIC-FL: Quick Unbiased Compression for Federated Learning
Distributed Mean Estimation (DME), in which $n$ clients communicate vectors to a parameter server that estimates their average, is a fundamental building block in communication-efficient federated learning.
EDEN: Communication-Efficient and Robust Distributed Mean Estimation for Federated Learning
Distributed Mean Estimation (DME) is a central building block in federated learning, where clients send local gradients to a parameter server for averaging and updating the model.
DRIVE: One-bit Distributed Mean Estimation
We consider the problem where $n$ clients transmit $d$-dimensional real-valued vectors using $d(1+o(1))$ bits each, in a manner that allows the receiver to approximately reconstruct their mean.
Cheetah: Accelerating Database Queries with Switch Pruning
In this paper, we leverage programmable switches in the network to partially offload query computation to the switch.
Databases Networking and Internet Architecture
Learning Software Constraints via Installation Attempts
Modern software systems are expected to be secure and contain all the latest features, even when new versions of software are released multiple times an hour.
