Dynamic Network Topology Updates for Distributed Machine Learning
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Solution Overview
Problem
Current distributed machine learning systems face inefficiencies in communication and resource allocation, particularly in dynamic environments, leading to higher costs and limited applicability due to static network designs that do not account for changing latency and bandwidth conditions.
Innovation Solution
Dynamically determine resource allocation and weights for combining node models during training by using an edge server to generate and update control values for worker nodes, optimizing communication links and reducing straggler impact through intelligent network topology adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a more connected network is used to reduce iterations in model training, then training convergence speed is improved, but communication cost in each iteration increases
Solution Approach 1:
The patent applies dynamics by making the network topology adjustable rather than static. The system dynamically reconfigures the communication graph and consensus weight matrix based on real-time network conditions, allowing it to adapt between highly connected topologies (for faster convergence) and sparser topologies (for lower communication cost) depending on current requirements
Solution Approach 2:
The system changes parameters of the communication network including the consensus weight matrix elements and network topology structure. By adjusting these parameters dynamically based on network conditions and training progress, the system optimizes the trade-off between convergence speed and communication overhead
2Device complexity
If static network topology is used, then system complexity is reduced, but adaptability to dynamic environments deteriorates
Solution Approach 1:
The system implements self-service by automatically detecting changes in network conditions and dynamically reconfiguring its own topology without external intervention. The distributed nodes collectively adjust the consensus weight matrix and communication graph based on observed latency and bandwidth changes, enabling the network to self-adapt to dynamic environments
Solution Approach 2:
The system uses feedback from network performance measurements (latency, bandwidth utilization) to continuously adjust the network topology and consensus parameters. This closed-loop control enables the system to respond to environmental changes while maintaining optimal training performance
3Area of stationary object
If all physical links are utilized in static network, then communication coverage is maximized, but communication efficiency deteriorates due to bandwidth sharing
Solution Approach 1:
The patent applies segmentation by dividing the set of all possible communication links into active and inactive subsets. The system selectively activates only the necessary links in the communication graph based on current training needs and network conditions, rather than maintaining all possible connections, thereby improving communication efficiency while maintaining adequate coverage
Solution Approach 2:
The system uses partial action by activating only a subset of available communication links rather than all physical links. This selective activation reduces bandwidth contention and communication overhead while maintaining sufficient connectivity for model convergence
Data Source
AI summary
Methods, systems and computer program products for distributed machine learning are provided. Such methods, systems and products may comprise, or may comprise instructions operable to configure one or more processors to perform, a set of acts. Such acts may comprise a plurality of nodes performing a set of training round activities, and a server performing a set of network topology design activities. The network topology design activities may comprise the server generating update data based on data from the plurality of nodes. The plurality of nodes may use that data to update control values used to exchange and combine machine learning models. After the set of training round activities and the set of network topology design activities have been repeated one or more times, the plurality of nodes may send machine learning models to the server, and the server may use them to create an aggregated machine learning model.