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4.7.1.2 Ability to Ensure Better Data Privacy and Security by Training Algorithms on Decentralized Devices 4.7.2 Restraints 4.7.3 Opportunities 4.7.3.1 Potential to Enable Companies to Leverage a Shared Ml Model Collaboratively by Keeping Data on Devices 4.7.3.2 Capability to Enable Predictive Features on Smart Devices Without Impacting User Experience and Leaking 4.7.4 Challenges 4.7.4.1 Issues of High Latency and Communication Inefficiency 4.7.4.2 System Heterogeneity and Issue in Interoperability 4.7.4.3 Indirect Information Leakage 4.9 Use Case Analysis 4.9.1 WeBank and a Car Rental Service Provider Enable Insurance Industry to Reduce Data Traffic Violations Through Federated Learning 4.9.2 Federated Learning Enable Healthcare Companies to Encrypt and Protect Patient Data 4.9.3 WeBank and Extreme Vision Introduced Online Visual Object Detection Platform Powered by Federated Learning to Store Data in Cloud

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