mlsanityData to 14 Oct 2026

DISCO: A Browser-Based Privacy-Preserving Framework for Distributed Collaborative Learning

arXiv 2511.19750 ↗First posted 24 Nov 2025cs.LGcited by 0

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Abstract (arXiv)

Data is often impractical to share for a range of well considered reasons, such as concerns over privacy, intellectual property, and legal constraints. This not only fragments the statistical power of predictive models, but creates an accessibility bias, where accuracy becomes inequitably distributed to those who have the resources to overcome these concerns. We present DISCO: an open-source DIStributed COllaborative learning platform accessible to non-technical users, offering a means to collaboratively build machine learning models without sharing any original data or requiring any programming knowledge. DISCO's web application trains models locally directly in the browser, making our tool cross-platform out-of-the-box, including smartphones. The modular design of disco offers choices between federated and decentralized paradigms, various levels of privacy guarantees and several approaches to weight aggregation strategies that allow for model personalization and bias resilience in the collaborative training. Code repository is available at https://github.com/epfml/disco and a showcase web interface at https://discolab.ai

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 16 of its 36 references that are in the map. First posted on arXiv 24 Nov 2025.

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References · 16

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  3. 2016Deep Learning with Differential Privacycited by 2,728
  4. 2018Federated Learning with Non-IID Datacited by 1,108
  5. 2020Inverting Gradients -- How easy is it to break privacy in federated learning?cited by 712
  6. 2016Federated Optimization: Distributed Machine Learning for On-Device Intelligencecited by 564
  7. 2019Federated Learning with Differential Privacy: Algorithms and Performance Analysiscited by 536
  8. 2017Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descentcited by 502
  9. 2020Flower: A Friendly Federated Learning Research Frameworkcited by 455
  10. 2018Confounding variables can degrade generalization performance of radiological deep learning modelscited by 379
  11. 2019SecureBoost: A Lossless Federated Learning Frameworkcited by 176
  12. 2020Learning from History for Byzantine Robust Optimizationcited by 137
4 more
  1. 2022NVIDIA FLARE: Federated Learning from Simulation to Real-Worldcited by 80
  2. 2022Fishing for User Data in Large-Batch Federated Learning via Gradient Magnificationcited by 73
  3. 2021SensiX++: Bringing MLOPs and Multi-tenant Model Serving to Sensory Edge Devicescited by 7
  4. 2020SensiX: A Platform for Collaborative Machine Learning on the Edgecited by 2

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