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resources about federated learning and privacy in machine learning
| Date | Stars |
|---|---|
| 2026-07-31 | 545 |
| 2026-08-06 | 545 |
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# Awesome Federated Learning [](https://awesome.re) A list of resources releated to federated learning and privacy in machine learning. ## Related Awesome Lists * [tushar-semwal/awesome-federated-computing](https://github.com/tushar-semwal/awesome-federated-computing) ## Papers ### Introduction & Survey * Towards Efficient Synchronous Federated Training: A Survey on System Optimization Strategies https://ieeexplore.ieee.org/document/9780218 * The Internet of Federated Things (IoFT) https://ieeexplore.ieee.org/document/9611259 * Advances and Open Problems in Federated Learning https://arxiv.org/pdf/1912.04977.pdf * Federated Machine Learning: Concept and Applications https://arxiv.org/pdf/1902.04885 * Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection https://arxiv.org/abs/1907.09693 * Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis https://arxiv.org/abs/1802.09941 * EdgeAI: A Visionfor Deep Learning in IoT Era https://arxiv.org/abs/1910.10356 * Machine Learning Systems for Highly-Distributed and Rapidly-Growing Data https://arxiv.org/abs/1910.08663 * No Peek: A Survey of private distributed deep learning https://arxiv.org/pdf/1812.03288 * Federated Learning in Mobile Edge Networks: A Comprehensive Survey https://arxiv.org/abs/1909.11875 ### Privacy and Security * Federated Learning with Formal Differential Privacy Guarantees https://ai.googleblog.com/2022/02/federated-learning-with-formal.html * Applying Differential Privacy to Large Scale Image Classification https://ai.googleblog.com/2022/02/applying-differential-privacy-to-large.html * Towards Causal Federated Learning For Enhanced Robustness And Privacy https://arxiv.org/pdf/2104.06557.pdf ICLR DPML 2021 * FedAUX: Leveraging Unlabeled Auxiliary Data in Federated Learning https://arxiv.org/abs/2102.02514 * OpenFL: An open-source framework for Federated Learning https://arxiv.org/abs/2105.06413 * A Bayesian Federated Learning Framework with Multivariate Gaussian Product https://arxiv.org/abs/2102.01936 * Communication-Efficient Learning of Deep Networks from Decentralized Data https://arxiv.org/pdf/1602.05629.pdf * Practical Secure Aggregation for Federated Learning on User-Held Data https://arxiv.org/abs/1611.04482 * Practical Secure Aggregation for Privacy-Preserving Machine Learning https://storage.googleapis.com/pub-tools-public-publication-data/pdf/ae87385258d90b9e48377ed49d83d467b45d5776.pdf * A Hybrid Approach to Privacy-Preserving Federated Learning https://arxiv.org/abs/1812.03224 * Analyzing Federated Learning through an Adversarial Lens https://arxiv.org/pdf/1811.12470 * How To Backdoor Federated Learning https://arxiv.org/abs/1807.00459 * Comprehensive Privacy Analysis of Deep Learning: Stand-alone and Federated Learning under Passive and Active White-box Inference Attack https://arxiv.org/abs/1812.00910 * Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning https://arxiv.org/pdf/1812.00535 * Exploiting Unintended Feature Leakage in Collaborative Learning https://arxiv.org/abs/1805.04049 * Analyzing Federated Learning through an Adversarial Lens https://arxiv.org/abs/1811.12470 * Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning https://arxiv.org/abs/1702.07464 * Protection Against Reconstruction and Its Applications in Private Federated Learning https://arxiv.org/pdf/1812.00984 * Boosting Privately: Privacy-Preserving Federated Extreme Boosting for Mobile Crowdsensing https://arxiv.org/abs/1907.10218 * Differentially Private Data Generative Models https://arxiv.org/pdf/1812.02274 * Differentially Private Federated Learning: A Client Level Perspective https://arxiv.org/abs/1712.07557 * Privacy-Preserving Collaborative Deep Learning with Unreliable Participants https://arxiv.org/abs/1812.10113 * Scalable Private Learning with PATE https://arxiv.org
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Maria Boerner · Germany
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Alexey Gruzdev · Intel
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:8e60273aba642994, llm:README: 'A list of resources related to federated learning and privacy in machine learning'; topics: federated-learning, privacy, medical-data, deep-learning
matched fp:8e60273aba642994, llm:README: 'A list of resources related to federated learning and privacy in machine learning'; topics: federated-learning, privacy, medical-data, deep-learning