mlsanityData to 7 Oct 2026

Facebook Report on Privacy of fNIRS data

arXiv 2401.00973 ↗First posted 1 Jan 2024cs.LG cs.CRcited by 0

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Abstract (arXiv)

The primary goal of this project is to develop privacy-preserving machine learning model training techniques for fNIRS data. This project will build a local model in a centralized setting with both differential privacy (DP) and certified robustness. It will also explore collaborative federated learning to train a shared model between multiple clients without sharing local fNIRS datasets. To prevent unintentional private information leakage of such clients' private datasets, we will also implement DP in the federated learning setting.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 16 of its 46 references that are in the map. First posted on arXiv 1 Jan 2024.

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16 of 46 references.

References · 16

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019Advances and Open Problems in Federated Learningcited by 3,021
  3. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  4. 2016Deep Learning with Differential Privacycited by 2,682
  5. 2016Membership Inference Attacks against Machine Learning Modelscited by 2,221
  6. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  7. 2019Deep Leakage from Gradientscited by 1,004
  8. 2019Towards Federated Learning at Scale: System Designcited by 826
  9. 2020Inverting Gradients -- How easy is it to break privacy in federated learning?cited by 707
  10. 2016Stealing Machine Learning Models via Prediction APIscited by 602
  11. 2017Differentially Private Federated Learning: A Client Level Perspectivecited by 451
  12. 2021Opacus: User-Friendly Differential Privacy Library in PyTorchcited by 375
4 more
  1. 2017Machine Learning Models that Remember Too Muchcited by 165
  2. 2018Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learningcited by 75
  3. 2016Decentralized Collaborative Learning of Personalized Models over Networkscited by 73
  4. 2018Differentially-Private "Draw and Discard" Machine Learningcited by 7

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