Collaborative Machine Learning Model Exchange via Gossip Protocol
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In distributed computing networks like CDNs, individual nodes can only build local machine learning models based on their limited data, lacking a mechanism to efficiently augment their knowledge from peers for network optimization.
Innovation Solution
Nodes in the network exchange portions of their machine learning models using a gossip protocol or equivalent, enabling transfer learning and collaborative model convergence for enhanced network functionality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If individual nodes build local machine learning models using only their own data, then each node can operate independently with low communication overhead, but the model accuracy and knowledge coverage remain limited due to data scarcity at individual nodes
Solution Approach 1:
The patent merges machine learning models from multiple peer nodes through a gossip protocol, combining partial models into a more comprehensive augmented model. This merging process integrates knowledge from diverse data sources, thereby improving both model accuracy and knowledge coverage without requiring centralized data collection.
Solution Approach 2:
The patent introduces a gossip protocol as an intermediary mechanism that enables indirect knowledge transfer between nodes. Instead of direct peer-to-peer model sharing, nodes exchange model portions through multiple hops, allowing knowledge to propagate throughout the network and be aggregated into augmented models with broader coverage.
2Adaptability or versatility
If nodes exchange complete machine learning models with peers, then knowledge sharing and model augmentation are maximized, but communication overhead and computational costs increase significantly
Solution Approach 1:
The patent segments the complete machine learning model into smaller partial models or model portions that can be independently exchanged between nodes. This segmentation reduces the communication burden on each node while still enabling comprehensive knowledge aggregation when partial models are combined, thereby improving adaptability without proportionally increasing device complexity.
Solution Approach 2:
The patent implements partial action by having nodes exchange only certain portions of their models rather than complete models. This selective sharing reduces communication overhead and computational requirements while still achieving sufficient knowledge diversity for effective model augmentation through the gossip protocol's iterative mixing process.
3Productivity
If frequent model updates and exchanges are performed across the network, then model convergence and optimization are accelerated, but network bandwidth consumption and processing time increase
Solution Approach 1:
The patent implements periodic action by having nodes perform model exchanges and updates at regular intervals rather than continuously. The gossip protocol naturally provides this periodic behavior through scheduled interactions, allowing models to converge over time while reducing instantaneous network load and processing requirements compared to continuous updates.
Solution Approach 2:
The patent applies preliminary action by pre-computing and preparing model portions for exchange before actual communication occurs. Nodes can prepare their model segments in advance during periods of low network activity, then perform faster exchanges when updates are needed, thereby accelerating convergence without proportionally increasing processing time during critical update moments.
Data Source
AI summary
Individual nodes (e.g., edge machines) in an overlay network each build local machine learning (ML) models associated with a particular behavior of interest. Through a communication mechanism, nodes exchange some portion of their ML models between or among each other. The portion of the local model that is exchanged with one or more other nodes encodes or encapsulates relevant knowledge (learned at the source node) for the particular behavior of interest; in this manner, relevant transfer learning is enabled such that individual node models become smarter. Sets of machines that collaborate converge their models toward a solution that is then used to facilitate another overlay network function or optimization. The local knowledge exchange among the nodes creates an emergent behavioral profile used to control the edge machine behavior. Example functions managed with this ML front-end include predictive pre-fetching, anomaly detection, image management, forecasting to allocate resources, and others.