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Found 8 papers, 2 papers with code
Federated Learning Over Images: Vertical Decompositions and Pre-Trained Backbones Are Difficult to Beat
We carefully evaluate a number of algorithms for learning in a federated environment, and test their utility for a variety of image classification tasks.
Chain-Of-Thought Prompting Under Streaming Batch: A Case Study
Recently, Large Language Models (LLMs) have demonstrated remarkable capabilities.
Auto-Differentiation of Relational Computations for Very Large Scale Machine Learning
The relational data model was designed to facilitate large-scale data management and analytics.
Compress, Then Prompt: Improving Accuracy-Efficiency Trade-off of LLM Inference with Transferable Prompt
Thus, optimizing this accuracy-efficiency trade-off is crucial for the LLM deployment on commodity hardware.
Federated Multiple Label Hashing (FedMLH): Communication Efficient Federated Learning on Extreme Classification Tasks
Federated learning enables many local devices to train a deep learning model jointly without sharing the local data.
Tensor Relational Algebra for Machine Learning System Design
This implementation abstraction provides little built-in support for ML systems to scale past a single machine, or for handling large models with matrices or tensors that do not easily fit into the RAM of an ASIC.
Distributed Learning of Deep Neural Networks using Independent Subnet Training
These properties of IST can cope with issues due to distributed data, slow interconnects, or limited device memory, making IST a suitable approach for cases of mandatory distribution.
Programmable In-Network Security for Context-aware BYOD Policies
Recent work has developed SDN solutions to collect device context for network-wide access control in a central controller.
Networking and Internet Architecture Cryptography and Security
