Federated Learning with Formal User-Level Differential Privacy Guarantees

Federated Learning with Formal User-Level Differential Privacy Guarantees

TheIACR via YouTube Direct link

Deconstructing the SGD model update

9 of 20

9 of 20

Deconstructing the SGD model update

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Federated Learning with Formal User-Level Differential Privacy Guarantees

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  1. 1 Intro
  2. 2 (Non)-convex learning
  3. 3 Differential Privacy
  4. 4 Cross-device federated learning
  5. 5 Differentially private stochastic gradient descent DP
  6. 6 DP-SGD: Key insights
  7. 7 DP-Federated Averaging (DP-FedAvg)
  8. 8 Challenges for Amplification by Sampling in FL
  9. 9 Deconstructing the SGD model update
  10. 10 Noise Accumulation in Prefix Sums
  11. 11 Towards Tree Aggregation
  12. 12 Interlude: Follow-the-regularized-leader (FTRL)
  13. 13 DP-Follow-the-regularized leader (DP-FTRL)
  14. 14 DP-FTRL: Online learning properties
  15. 15 Privacy-Utility Trade-offs for Stackoverflow
  16. 16 Production model with formal DP
  17. 17 Matrix factorization view of prefix sum estimation
  18. 18 Matrix factorization view of DP prefix sum
  19. 19 Future directions
  20. 20 Acknowledgements

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