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Found 7 papers, 3 papers with code
Navigating Data Heterogeneity in Federated Learning A Semi-Supervised Federated Object Detection
Federated Learning (FL) has emerged as a potent framework for training models across distributed data sources while maintaining data privacy.
Does fine-tuning GPT-3 with the OpenAI API leak personally-identifiable information?
In this work, we simulate a privacy attack on GPT-3 using OpenAI's fine-tuning API.
oneDNN Graph Compiler: A Hybrid Approach for High-Performance Deep Learning Compilation
We present oneDNN Graph Compiler, a tensor compiler that employs a hybrid approach of using techniques from both compiler optimization and expert-tuned kernels for high performance code generation of the deep neural network graph.
TinyFedTL: Federated Transfer Learning on Tiny Devices
TinyML has rose to popularity in an era where data is everywhere.
FedFMC: Sequential Efficient Federated Learning on Non-iid Data
These experiments show that FedFMC substantially improves upon earlier approaches to non-iid data in the federated learning context without using a globally shared subset of data nor increase communication costs.
FedCD: Improving Performance in non-IID Federated Learning
Federated learning has been widely applied to enable decentralized devices, which each have their own local data, to learn a shared model.
Robotic Room Traversal using Optical Range Finding
Our room traversal algorithm relies upon the approximate distance from the robot to the nearest obstacle in 360 degrees.
