Collection of open machine learning papers
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Machine learning papers
- Missing data
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Join the community, add a new evaluation result row, federated learning.
1542 papers with code • 12 benchmarks • 11 datasets
Federated Learning is a machine learning approach that allows multiple devices or entities to collaboratively train a shared model without exchanging their data with each other. Instead of sending data to a central server for training, the model is trained locally on each device, and only the model updates are sent to the central server, where they are aggregated to improve the shared model.
This approach allows for privacy-preserving machine learning, as each device keeps its data locally and only shares the information needed to improve the model.
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Most implemented papers
Communication-efficient learning of deep networks from decentralized data.
Modern mobile devices have access to a wealth of data suitable for learning models, which in turn can greatly improve the user experience on the device.
Federated Optimization in Heterogeneous Networks
Theoretically, we provide convergence guarantees for our framework when learning over data from non-identical distributions (statistical heterogeneity), and while adhering to device-level systems constraints by allowing each participating device to perform a variable amount of work (systems heterogeneity).
Adaptive Personalized Federated Learning
Investigation of the degree of personalization in federated learning algorithms has shown that only maximizing the performance of the global model will confine the capacity of the local models to personalize.
Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification
In this work, we look at the effect such non-identical data distributions has on visual classification via Federated Learning.
Advances and Open Problems in Federated Learning
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.
LEAF: A Benchmark for Federated Settings
TalwalkarLab/leaf • 3 Dec 2018
Modern federated networks, such as those comprised of wearable devices, mobile phones, or autonomous vehicles, generate massive amounts of data each day.
Agnostic Federated Learning
litian96/fair_flearn • 1 Feb 2019
A key learning scenario in large-scale applications is that of federated learning, where a centralized model is trained based on data originating from a large number of clients.
Towards Federated Learning at Scale: System Design
Federated Learning is a distributed machine learning approach which enables model training on a large corpus of decentralized data.
FedMD: Heterogenous Federated Learning via Model Distillation
With 10 distinct participants, the final test accuracy of each model on average receives a 20% gain on top of what's possible without collaboration and is only a few percent lower than the performance each model would have obtained if all private datasets were pooled and made directly available for all participants.
SCAFFOLD: Stochastic Controlled Averaging for Federated Learning
We obtain tight convergence rates for FedAvg and prove that it suffers from `client-drift' when the data is heterogeneous (non-iid), resulting in unstable and slow convergence.
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A list of research papers in the domain of machine learning, deep learning and related fields. I have curated a list of research papers that I come across and read. I'll keep on updating the list of papers and their summary as I read them every week.
2019-10-28 Started must-read-papers-for-ml repo. 2019-10-29 Added analytics vidhya use case studies article links. 2019-10-30 Added Outlier/Anomaly detection paper, separated Boosting, CNN, Object Detection, NLP papers, and added Image captioning papers. 2019-10-31 Added Famous Blogs from Deep and Machine Learning Researchers
GitHub is where people build software. More than 100 million people use GitHub to discover, fork, and contribute to over 420 million projects. ... A collection of research papers on decision, classification and regression trees with implementations. ... To associate your repository with the machine-learning-research topic, visit ...
Papers With Code highlights trending Machine Learning research and the code to implement it. Browse State-of-the-Art Datasets ; Methods; More ... Subscribe to the PwC Newsletter ×. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. Read previous issues. Subscribe.
Papers With Code highlights trending Machine Learning research and the code to implement it. Browse State-of-the-Art Datasets ; Methods; More ... For every open access machine learning paper, we check whether a code implementation is available on GitHub. The date axis is the publication date of the paper.
Machine Learning papers (landing page) mlpapers. Collection of open machine learning papers. View on GitHub mlpapers/mlpapers.github.io. Follow on Twitter @mlpapers. Machine learning papers
If so, this GitHub repository, a clearinghouse for research papers and their corresponding implementation code, is definitely worth checking out. By Matthew Mayo , KDnuggets Managing Editor on December 31, 2018 in GitHub , Machine Learning , Research
**Federated Learning** is a machine learning approach that allows multiple devices or entities to collaboratively train a shared model without exchanging their data with each other. Instead of sending data to a central server for training, the model is trained locally on each device, and only the model updates are sent to the central server, where they are aggregated to improve the shared model.
Related Machine learning Computer science Information & communications technology Applied science Formal science Technology Science forward back r/cpp Discussions, articles and news about the C++ programming language or programming in C++.
GitHub is where people build software. More than 100 million people use GitHub to discover, fork, and contribute to over 420 million projects. ... nlp machine-learning deep-learning paper transfer-learning research-paper machine-learning-papers annotated-papers Updated Nov 11, 2022; dylan ... To associate your repository with the machine ...