P2P Mesh Network Active Learning Latency Reduction
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Solution Overview
Problem
Traditional active learning systems face significant latency and inefficiencies in delivering anomalous data to subject matter experts (SMEs) and returning labeled data to the ML model, especially in decentralized environments without reliable internet connectivity.
Innovation Solution
The implementation of a peer-to-peer (P2P) mesh network that synchronizes database updates across peers, allowing for quick propagation of anomalous data and labeled data, even in the absence of internet connectivity, thereby reducing latency and improving the active learning process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional centralized active learning systems are used, then ML model retraining can be performed, but significant latency occurs in delivering anomalous data to SMEs and returning labeled data, especially in decentralized environments without reliable internet connectivity
Solution Approach 1:
The system segments the centralized active learning process into distributed peer-to-peer operations. Each peer maintains local anomaly detection capabilities and local database versions, allowing the system to function autonomously in disconnected environments while maintaining collaboration through selective data sharing when connected.
Solution Approach 2:
The P2P mesh network acts as an intermediary layer between peers, enabling direct communication and data propagation without requiring centralized cloud infrastructure. This intermediary network allows anomalies and labeled data to propagate peer-to-peer, reducing latency and enabling operation without reliable internet connectivity.
2Productivity
If centralized cloud-based systems are used for active learning, then ML models can be retrained with labeled data, but the system requires reliable internet connectivity and suffers from latency in data propagation
Solution Approach 1:
Peers perform preliminary anomaly detection and local database updates independently before needing cloud connectivity. The system prepares labeled data and anomaly information locally, so when internet connectivity is available, the already-prepared data can be quickly propagated to update the centralized model without waiting for cloud-based processing.
Solution Approach 2:
The system enables self-service operation where peers autonomously detect anomalies, maintain local database versions, and propagate findings to other peers without requiring centralized cloud coordination. This self-service capability ensures continuous operation during internet outages while maintaining system-wide consistency through peer-to-peer synchronization.
3Loss of time
If real-time collaboration among distributed peers is enabled, then active learning efficiency improves, but complex P2P mesh network synchronization is required
Solution Approach 1:
Instead of complex real-time synchronization of entire databases, the system uses copy-based propagation where peers transmit copies of specific anomaly records and labeled data to other peers. This copying approach simplifies the synchronization protocol, as peers only need to propagate specific data changes rather than maintain complex state consistency, reducing the overall system complexity while enabling rapid information dissemination.
Data Source
AI summary
Techniques for facilitating active learning of an ML model are disclosed. Raw data is fed to the ML model. The ML model identifies a portion of the raw data as being anomalous. A first peer updates a local version of a database associated with a P2P mesh network. The update includes a new database entry. The new entry reflects the anomalous data and constitutes a database delta change. The first peer propagates the delta change to a second peer in the P2P mesh network. Later, the first peer receives, from the second peer, a second delta change to the database. The second delta change includes label data for the anomalous data. The first peer updates its local database version to include the second delta change, resulting in the anomalous data now being labeled locally. The first peer retrains the ML model based on the newly labeled data.