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Found 14 papers, 5 papers with code
Lucy: Think and Reason to Solve Text-to-SQL
Large Language Models (LLMs) have made significant progress in assisting users to query databases in natural language.
Beyond Throughput and Compression Ratios: Towards High End-to-end Utility of Gradient Compression
Gradient aggregation has long been identified as a major bottleneck in today's large-scale distributed machine learning training systems.
Optimal and Near-Optimal Adaptive Vector Quantization
Quantization is a fundamental optimization for many machine-learning use cases, including compressing gradients, model weights and activations, and datasets.
THC: Accelerating Distributed Deep Learning Using Tensor Homomorphic Compression
To address this bottleneck and accelerate training, a widely-deployed approach is compression.
DoCoFL: Downlink Compression for Cross-Device Federated Learning
Many compression techniques have been proposed to reduce the communication overhead of Federated Learning training procedures.
ScionFL: Efficient and Robust Secure Quantized Aggregation
In this paper, we unite both research directions by introducing ScionFL, the first secure aggregation framework for FL that operates efficiently on quantized inputs and simultaneously provides robustness against malicious clients.
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.
Automating In-Network Machine Learning
To date, no general solution has been provided for mapping machine learning algorithms to programmable network devices.
IIsy: Practical In-Network Classification
In this paper, we introduce IIsy, implementing machine learning classification models in a hybrid fashion using off-the-shelf network devices.
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.
How to send a real number using a single bit (and some shared randomness)
We consider both the biased and unbiased estimation problems and aim to minimize the cost.
RADE: Resource-Efficient Supervised Anomaly Detection Using Decision Tree-Based Ensemble Methods
Decision-tree-based ensemble classification methods (DTEMs) are a prevalent tool for supervised anomaly detection.
AnchorHash: A Scalable Consistent Hash
Consistent hashing (CH) is a central building block in many networking applications, from datacenter load-balancing to distributed storage.
Data Structures and Algorithms
