Distributed Average Consensus for Privacy-Preserving Recommendations
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Solution Overview
Problem
Current distributed computing systems face challenges in verifying the contribution of computing devices to calculations, leading to potential compensation without actual work being performed, and centralized AI model updates require significant central server resources without leveraging distributed computing power, while centralized personalized recommendation systems infringe on user data privacy.
Innovation Solution
Implementing a distributed average consensus (DAC) algorithm that allows computing devices to exchange data peer-to-peer, confirming participation through consensus, and using a cooperative subspace approach with random sampling to generate personalized recommendations without exposing personal data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a centralized system is used to collect and process user preference data for generating personalized recommendations, then the system can provide accurate personalized recommendations, but user data privacy is compromised as the centralized system collects, stores, and distributes personal data
Solution Approach 1:
The system segments the centralized recommendation system into multiple distributed computing devices, each holding local user preference data. These devices collaboratively compute recommendations through peer-to-peer communication without a central server collecting personal data. The segmentation allows the system to maintain recommendation accuracy while preserving user privacy, as each device processes data locally and only exchanges computed results rather than raw personal information.
2Reliability
If a centralized server is used to update AI models using data from distributed computers, then the AI model can be updated with comprehensive data, but the central server requires significant computing resources and does not leverage distributed computing power
Solution Approach 1:
The system merges the AI model updating process across multiple distributed computing devices, allowing each device to contribute to model updates using its local data. Instead of centralizing all data processing on a single server, the updating process is distributed while maintaining coordination through the DAC algorithm. This combining approach enables the system to leverage the computing power of all distributed devices while still achieving comprehensive model updates.
Solution Approach 2:
The system transitions from a vertical centralized processing architecture to a horizontal distributed processing architecture. By adding the dimension of peer-to-peer communication and collaboration among distributed devices, the system can perform AI model updates in parallel across multiple nodes, significantly reducing the computing burden on any single server while maintaining model update accuracy through consensus mechanisms.
3Reliability
If typical distributed computing operations are used that allow fault tolerance, then the system can converge even if some computing devices fail, but the system cannot verify whether all computing devices actually performed their assigned computation
Solution Approach 1:
The system implements a feedback mechanism through the DAC algorithm where each computing device reports its computed portion to the group, and the consensus process verifies that all devices have contributed. The algorithm provides feedback about participation status, allowing the system to confirm that all devices performed their assigned computation while maintaining fault tolerance. This feedback loop resolves the verification problem without adding significant complexity to the distributed computing operation.
Data Source
AI summary
A distributed computing device stores user preference data representing preferences of a user with respect to a portion of a set of items. The distributed computing device randomly samples the user preference data to calculate sampled user preference data. The distributed computing device iteratively executes, in conjunction with additional distributed computing devices connected by a network, a process to determine a consensus result for the sampled user preference data. The consensus result is based on the sampled user preference data and additional sampled user preference data calculated by the additional distributed computing devices and based on preferences of additional users. The distributed computing device determines a recommendation model based on the consensus result, the recommendation model reflecting preferences of the user and additional users. The distributed computing device identifies an item to provide to the user as a recommendation based on the recommendation model, and provides recommendation to the user.