Edge Node Distributed Machine Learning Parameter Aggregation
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Solution Overview
Problem
Current distributed machine learning methods face challenges in efficiently processing large datasets at the edge due to increasing data volumes and decreasing communication bandwidth, necessitating a solution that enhances speed, reduces bandwidth consumption, and optimizes resource utilization without exchanging bulky data.
Innovation Solution
A method for distributed machine learning that involves a model requester node generating a specification, distributing it to edge nodes, receiving updates, and aggregating parameters without exchanging training data, while selectively retaining nodes based on learning utility and cost estimates to optimize resource allocation and reduce bandwidth usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If distributed machine learning is implemented across multiple edge nodes, then processing capacity and model training capability are improved, but communication bandwidth consumption increases due to parameter exchange
Solution Approach 1:
The patent extracts only the essential model parameters that need to be exchanged between nodes, rather than transmitting entire models or raw data. This selective extraction of critical parameters significantly reduces communication overhead while maintaining training effectiveness.
Solution Approach 2:
The model training process is segmented into discrete parameter updates that are exchanged between nodes. Each node processes local data independently and contributes incremental parameter updates, allowing parallel processing without requiring continuous full-model synchronization.
2Measurement precision
If more edge nodes are selected for collaborative training, then model accuracy and generalization are improved, but system complexity and coordination overhead increase
Solution Approach 1:
The system implements feedback mechanisms where nodes receive aggregated parameter updates from the central server and adjust their local models accordingly. This feedback loop enables coordinated training across multiple nodes while maintaining simple individual node operations.
Solution Approach 2:
The patent designs a universal parameter exchange protocol that works across heterogeneous edge nodes with different computational capabilities and data types. This universal interface simplifies coordination by providing a standardized method for all nodes to contribute to the global model.
3Stability of the object's composition
If synchronous training is used across distributed nodes, then model consistency is improved, but training speed decreases due to waiting for slowest nodes
Solution Approach 1:
The system uses periodic synchronization where nodes exchange parameters at fixed intervals rather than continuously. This periodic action allows nodes to proceed independently between synchronization points, improving speed while maintaining consistency through regular updates.
Solution Approach 2:
Nodes perform preliminary local training updates before synchronization events. This preliminary action allows each node to make progress independently, and the subsequent synchronization consolidates these updates, eliminating the need to wait for the slowest node during entire training cycles.
Data Source
AI summary
A model requester node, which is an edge node of a cloud computing network, generates a specification of a machine learning model, distributes the specification to a plurality of other edge nodes, and receives replies to the specification from the plurality of other edge nodes. In response to the replies, the model requester node identifies a set of participating edge nodes based on a learning utility and a cost estimate of each of the plurality of other edge nodes. The model requester node then trains the machine learning model, without exchanging training data among the model requester node and the participating edge nodes, by repeatedly: distributing most recent parameters of the machine learning model to the participating edge nodes; receiving updates to the most recent parameters from the participating edge nodes; and establishing new parameters for the machine learning model by aggregating the updates from the participating edge nodes.