Federated Learning Behavior Detection on Electronic Devices
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Solution Overview
Problem
Existing electronic devices are limited in detecting dynamic human behaviors and violate user privacy by requiring data storage on servers, failing to decentralize behavior curation and capture emotion correlations effectively.
Innovation Solution
A Federated Learning approach using smart device usage data to detect user behaviors, predict next actions, and provide behavioral recommendations while maintaining user privacy through joint probability distribution tables and emotion correlation analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data is stored on server from user's electronic device for training pre-trained models, then behavior detection capability is improved, but user privacy is violated
Solution Approach 1:
The system segments the behavior detection process into distributed components: each electronic device performs local behavior detection and emotion recognition independently, while only aggregated statistics are shared with the server. This segmentation eliminates the need to store raw user data on the server, resolving the privacy violation issue while maintaining detection capability through local processing and decentralized learning.
2Ease of operation
If pre-trained models are used for detecting basic activities, then detection functionality is provided, but dynamic human behaviors cannot be captured
Solution Approach 1:
The system transitions from static pre-trained models to dynamic emotion-correlation-based detection. Each electronic device continuously learns from local emotion data and updates its behavior detection capabilities in real-time. The server aggregates these updates to create evolving detection models that adapt to new and dynamic human behaviors, enabling the system to capture both basic activities and complex dynamic behaviors effectively.
3Reliability
If centralized server processes all user data for behavior analysis, then comprehensive behavior curation is achieved, but device complexity and data security risks increase
Solution Approach 1:
Each electronic device performs self-service behavior detection and emotion recognition using its own sensors and processors. The devices independently curate their own behavior data and generate local models without requiring constant server intervention. This self-service approach reduces reliance on centralized processing, simplifying the overall system architecture while maintaining comprehensive behavior curation through distributed contribution of local insights to the global model.
Data Source
AI summary
Embodiments herein disclose methods and systems for identifying behavioural trends across users. The system includes electronic devices. The electronic devices include a behavioural recommendation controller. The behavioural recommendation controller is configured to: detect a first plurality of activities performed by a plurality of first users in relation with a plurality of contexts; recognize the first plurality of physical activities in relation with the plurality of contexts for the first user; recognize multiple activities performed using smart devices by the first user during each first physical activity in each context; and recognize a second plurality of physical activities performed by multiple concurrent second users during each context to refer current behavior or new behavior of the users.