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Sticker Recommendation Using Federated Learning

Sticker Recommendation Using Federated Learning

Haruka Kikuchi (LINE / Machine Learning Platform Department / Product Manager)

https://tech-verse.me/ja/sessions/46
https://tech-verse.me/en/sessions/46
https://tech-verse.me/ko/sessions/46

Tech-Verse2022
PRO

November 17, 2022
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Transcript

  1. None
  2. Who I Am - Haruka Kikuchi - Product Manager (ML)

    - Past Experience - Security Research (Prog. Lang.) - Human-Computer Interaction Research - Geo-spatial Data Analysis, etc.
  3. Agenda - What is Federated Learning (FL)? - 1st Target

    Application - ML Model Overview - LFL - LINE’s FL Platform - Privacy Preservation - Summary
  4. Server-side Machine Learning (ML) Centralized server(s) collect data and process

    ML Output Output Output Output Output Output Output Output Training Inference ML
  5. Server-side Machine Learning (ML) Centralized server(s) collect data and process

    ML Log Log Log Log Log Log Log Log Training Inference ML
  6. On-Device ML Inferencing Client devices receive global ML model and

    run inference ML Training Global Model Global Model Global Model Global Model Global Model Global Model Global Model Global Model
  7. On-Device ML Inferencing Client devices receive global ML model and

    run inference ML Training Global Model Global Model Global Model Global Model Global Model Global Model Global Model Global Model Inference Inference Inference Inference Inference Inference
  8. Training Training Training Training Training Federated Learning (FL) Client On-device

    ML training + server aggregation ML Training Training Training
  9. Training Training Training Training Training Federated Learning (FL) Client On-device

    ML training + server aggregation ML Training Training Training
  10. Federated Learning (FL) Global model are sent to individual devices

    ML Model Aggregation
  11. Federated Learning (FL) Global model are sent to individual devices

    ML Global Model Global Model Global Model Global Model Global Model Global Model Global Model Global Model Model Aggregation
  12. Federated Learning (FL) Global model are sent to individual devices

    ML Global Model Global Model Global Model Global Model Global Model Global Model Global Model Global Model Inference Inference Inference Inference Inference Inference Model Aggregation
  13. Section Summary Server-side ML ! resourceful Lots of data /

    computation resource Recommendation
  14. Section Summary Server-side ML ! resourceful Lots of data /

    computation resource On-Device ML Inferencing ! responsive no network latency Recommendation User Interface
  15. Section Summary Server-side ML ! resourceful Lots of data /

    computation resource On-Device ML Inferencing ! responsive no network latency Federated Learning ! privacy preservation users don’t have to send raw data to server Recommendation User Interface Sensitive Data Treatment
  16. Agenda - What is Federated Learning (FL)? - 1st Target

    Application - ML Model Overview - LFL - LINE’s FL Platform - Privacy Preservation - Summary
  17. - Sticker suggestions based on semantic labels - Incremental suggestions

    while text input, using pre-defined keywords associated with the each label Sticker Auto Suggest
  18. Sticker Semantic Tags https://creator.line.me

  19. Sticker Semantic Tags https://creator.line.me

  20. Stickers Premium Subscription Service https://store.line.me/stickers-premium/landing/en

  21. Stickers Premium Subscription Service https://store.line.me/stickers-premium/landing/en

  22. Available Stickers in Auto-Suggest Area Purchased, etc. User- downloaded Auto-

    downloaded
  23. Available Stickers in Auto-Suggest Area Purchased, etc. User- downloaded Auto-

    downloaded
  24. Agenda - What is Federated Learning (FL)? - 1st Target

    Application - ML Model Overview - LFL - LINE’s FL Platform - Privacy Preservation - Summary
  25. Hybrid Approach Server-side ML ! resourceful Lots of data /

    computation resource ! responsive no network latency Federated Learning ! privacy preservation users don’t have to send raw data to server Recommendation User Interface Communication Info. On-Device ML Inferencing Server-side ML Federated Learning
  26. Hybrid Approach Server-side ML ! resourceful Lots of data /

    computation resource ! responsive no network latency Federated Learning ! privacy preservation users don’t have to send raw data to server Recommendation User Interface Communication Info. Candidate Generation (1st Stage) Reranking (2nd Stage) On-Device ML Inferencing Server-side ML Federated Learning
  27. 1st Stage: Candidate Generation - Input: - Sticker purchase/download log

    - User features (estimated demographics, etc.) - Output (intermediate) - Item embeddings - User embeddings - Final output - Item candidates (per user cluster) Input Final Output Intermediate Output
  28. 2nd Stage: Reranking - Input: - user embedding (fully personalized)

    - item embedding (for each candidate) - Output - score for each item - Client App. performs - inference, triggered by text input - training, triggered when device-idle (Personalized) Make use of intermediate output in 1st stage (Global) Input Output
  29. Candidate Generation (1st stage) Reranking (2nd stage) Personalization Sticker Candidates

    (1,000,000 → 100) Reorder stickers (100) Signal Sticker download (e.g. purchases) Sticker click/impression Inference Server Client device Training Server Client device (mostly) Comparison 1st stage vs. 2nd stage
  30. Agenda - What is Federated Learning (FL)? - 1st Target

    Application - ML Model Overview - LFL - LINE’s FL Platform - Privacy Preservation - Summary
  31. Requirements as a Platform LINE Federated Learning (LFL) - Model

    upload without user ID - Differential Privacy (DP) mechanism Privacy Preservation Support multiple on-device ML instances - Separation of app. specific implementations from common FL functions
  32. System Architecture Separation of service specific ML functions from FL

    platform
  33. System Architecture Quadrants

  34. System Architecture 2-Staged ML

  35. System Architecture On-device ML (2nd Stage)

  36. System Architecture On-device ML Inferencing (2nd Stage)

  37. System Architecture Federated Learning

  38. Other Platform Features ONNX for OS-agnostic ML Runtime FL Model/Param

    A/B Test Local Model Training in Background Candidate Generation For Sticker Sticker keyboard Model Training Scheduler Model/Feature Ver. Management
  39. Other Platform Features ONNX for OS-agnostic ML Runtime FL Model/Param

    A/B Test Local Model Training in Background Candidate Generation For Sticker Sticker keyboard Model Training Scheduler Model/Feature Ver. Management
  40. Please Check Tomorrow’s Session! Nov. 18th (Fri) 15:00-16:00 JST

  41. Agenda - What is Federated Learning (FL)? - 1st Target

    Application - ML Model Overview - LFL - LINE’s FL Platform - Privacy Preservation - Summary
  42. Privacy Preservation - Noise addition to the model on local

    devices (Local DP) Support Differential Privacy (DP) mechanism Minimization of data collection - Federated Learning (collect ML models on behalf of raw data) - Model upload by randomly-sampled users without user id
  43. System Architecture FL + Local DP

  44. FL with Local Differential Privacy Noise injection to local model

    • Aggregation • Transformation • Noise Injection Noisy Outputs Local Devices Server
  45. FL with Local Differential Privacy Noise injection to local model

    • Aggregation • Transformation • Noise Injection Noisy Outputs Local Devices Server Indistinguishable represented by ε ?
  46. FL with Local Differential Privacy Noise injection to local model

    • Aggregation • Transformation • Noise Injection Noisy Outputs Local Devices Server Indistinguishable represented by ε ? ?
  47. Current Status - As-is: set a weak value to evaluate

    the feasibility of FL - To-be: set a mature value that balances utility of FL and users’ privacy Seeking a privacy parameter ε Implementation of Local DP mechanisms with FL - Gaussian mechanism for local gradient (Local DP) - Averaging the aggregated local models (FL) - Local model upload without user id
  48. Agenda - What is Federated Learning (FL)? - 1st Target

    Application - ML Model Overview - LFL - LINE’s FL Platform - Privacy Preservation - Summary
  49. A/B Test Result 5.6% uplift - Personalized sticker suggestions evoke

    explicit premium sticker package downloads
  50. Collaborations Multiple Locations w/ 30+ Engineers

  51. Collaborations Multiple Locations w/ 30+ Engineers KOREA KOREA TOKYO TOKYO

    FUKUOKA TOKYO
  52. Future Work - Seeking for LDP configuration - Shuffling mechanism

    Make LFL as a true LINE’s privacy preservation platform Expand FL-based reranking to all the stickers - Currently, sticker premium is the only service