Benchmarks
Datasets
Greatest papers with code
A generic framework for privacy preserving deep learning
• OpenMined/PySyft
• 
We detail a new framework for privacy preserving deep learning and discuss its assets.
Generative Models for Effective ML on Private, Decentralized Datasets
ICLR 2020
• tensorflow/federated
• 
To improve real-world applications of machine learning, experienced modelers develop intuition about their datasets, their models, and how the two interact.
Federated Learning for Mobile Keyboard Prediction
We train a recurrent neural network language model using a distributed, on-device learning framework called federated learning for the purpose of next-word prediction in a virtual keyboard for smartphones.
FedML: A Research Library and Benchmark for Federated Machine Learning
Federated learning (FL) is a rapidly growing research field in machine learning.
Label Leakage and Protection in Two-party Split Learning
We first show that, norm attack, a simple method that uses the norm of the communicated gradients between the parties, can largely reveal the ground-truth labels from the participants.
Advances and Open Problems in Federated Learning
• FedML-AI/FedML
•
FL embodies the principles of focused data collection and minimization, and can mitigate many of the systemic privacy risks and costs resulting from traditional, centralized machine learning and data science approaches.
Group Knowledge Transfer: Federated Learning of Large CNNs at the Edge
NeurIPS 2020
• FedML-AI/FedML
•
However, the large model size impedes training on resource-constrained edge devices.
Central Server Free Federated Learning over Single-sided Trust Social Networks
• FedML-AI/FedML
•
However, in many social network scenarios, centralized federated learning is not applicable (e. g., a central agent or server connecting all users may not exist, or the communication cost to the central server is not affordable).
Learning Private Neural Language Modeling with Attentive Aggregation
• shaoxiongji/federated-learning
•
Federated learning (FL) provides a promising approach to learning private language modeling for intelligent personalized keyboard suggestion by training models in distributed clients rather than training in a central server.
LEAF: A Benchmark for Federated Settings
Modern federated networks, such as those comprised of wearable devices, mobile phones, or autonomous vehicles, generate massive amounts of data each day.
AUTONOMOUS VEHICLES FEDERATED LEARNING META-LEARNING MULTI-TASK LEARNING