Federated Learning System

A Federated Learning System is a distributed privacy-preserving machine learning system that can be implemented by a federated learning algorithm to solve federated learning tasks (without federated learning data centralization).

• AKA: Collaborative Learning System, Decentralized Machine Learning System, Privacy-Preserving ML System.
• Context:
  • It can typically coordinate Federated Learning Clients through federated learning orchestration protocols.
  • It can typically aggregate Federated Learning Model Updates through federated learning aggregation algorithms.
  • It can typically preserve Federated Learning Data Privacy through federated learning encryption methods.
  • It can typically manage Federated Learning Communication through federated learning network protocols.
  • It can typically maintain Federated Learning Model Consistency through federated learning synchronization mechanisms.
  • ...
  • It can often handle Federated Learning Heterogeneity through federated learning adaptation strategys.
  • It can often mitigate Federated Learning Attacks through federated learning security measures.
  • It can often optimize Federated Learning Efficiency through federated learning compression techniques.
  • It can often ensure Federated Learning Fairness through federated learning bias correction.
  • ...
  • It can range from being a Horizontal Federated Learning System to being a Vertical Federated Learning System, depending on its federated learning data partitioning.
  • It can range from being a Synchronous Federated Learning System to being an Asynchronous Federated Learning System, depending on its federated learning update timing.
  • It can range from being a Cross-Device Federated Learning System to being a Cross-Silo Federated Learning System, depending on its federated learning deployment scale.
  • It can range from being a Homogeneous Federated Learning System to being a Heterogeneous Federated Learning System, depending on its federated learning client diversity.
  • It can range from being a Centralized Federated Learning System to being a Decentralized Federated Learning System, depending on its federated learning coordination model.
  • ...
  • It can implement Federated Learning Differential Privacy through federated learning noise addition.
  • It can provide Federated Learning Secure Aggregation through federated learning cryptographic protocols.
  • It can enable Federated Learning Model Personalization through federated learning local adaptation.
  • It can support Federated Learning Incentive Mechanisms through federated learning contribution tracking.
  • It can facilitate Federated Learning Debugging through federated learning monitoring tools.
  • ...
• Examples:
  • Mobile Device Federated Learning Systems, such as:
    • Google Gboard Federated Learning System demonstrating federated learning keyboard prediction.
    • Apple Siri Federated Learning System implementing federated learning voice recognition.
    • Google Photos Federated Learning System utilizing federated learning on-device learning.
  • Healthcare Federated Learning Systems, such as:
    • NVIDIA Clara Federated Learning System for federated learning medical imaging.
    • Intel OpenFL Federated Learning System for federated learning healthcare collaboration.
    • Owkin Federated Learning System for federated learning drug discovery.
  • Financial Federated Learning Systems, such as:
    • WeBank Federated Learning System for federated learning credit scoring.
    • Ant Group Federated Learning System for federated learning risk assessment.
    • Google Pay Federated Learning System for federated learning fraud detection.
  • IoT Federated Learning Systems, such as:
    • Edge AI Federated Learning Systems for federated learning smart city applications.
    • Industrial IoT Federated Learning Systems for federated learning predictive maintenance.
  • ...
• Counter-Examples:
  • Centralized Healthcare Learning Systems, which lack federated learning distributed training.
  • Legal Document Analysis Systems, which lack federated learning privacy-preserving computation.
  • Financial Data Warehouse Systems, which lack federated learning edge processing.
  • Educational Assessment Systems, which lack federated learning collaborative learning.
  • Centralized Machine Learning Systems, which lack federated learning distributed training.
  • Data Warehouse Systems, which lack federated learning privacy preservation.
  • Cloud ML Platforms, which lack federated learning edge computation.
• See: Semi-Supervised Learning Task, Machine Learning, Automated Predictive Modeling (ML) Task, AI Governance Framework, Digital Banking Platform, Fintech Fraud Detection System, Behavioral Anomaly Detection System.

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