Decentralized Model Consensus Under Limited Communication Bandwidth
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Solution Overview
Problem
Existing distributed learning techniques require robust communication, centralized coordination, and high bandwidth, making them unsuitable for environments with unreliable communication and biased data sets.
Innovation Solution
Consensus driven learning (CDL) trains local models asynchronously and decentralized, sharing model parameters instead of training data, allowing for minimal communication requirements and intermittent connectivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If distributed learning is performed without centralized coordination, then system reliability improves, but communication requirements increase
Solution Approach 1:
The learning process is segmented into local model training at individual nodes and periodic parameter sharing among nodes. Each node independently trains its local model using local data, then shares parameter updates with neighboring nodes. This segmentation eliminates the need for centralized coordination while reducing communication bandwidth requirements compared to sharing full datasets.
Solution Approach 2:
Instead of sharing original training data between nodes, the system copies and shares only the model parameters (weights and biases). This copying approach maintains system reliability through decentralized operation while dramatically reducing the quantity of data that needs to be communicated between nodes.
2Quantity of substance
If model parameters are shared instead of training data, then communication bandwidth requirements decrease, but learning performance may deteriorate
Solution Approach 1:
Each node performs preliminary local training on its own data before parameter sharing. This preliminary action ensures that nodes develop competent local models independently, and the subsequent parameter sharing serves to refine and align these models rather than build them from scratch, maintaining learning performance while minimizing communication requirements.
Solution Approach 2:
The system implements feedback loops where nodes continuously share parameter updates with neighboring nodes and adjust their local models based on received updates. This iterative feedback process ensures that despite limited communication bandwidth, all nodes converge toward an optimal shared model, maintaining learning performance equivalent to centralized approaches.
3Stability of the object's composition
If synchronous synchronization is implemented during updates, then model consistency improves, but training time increases
Solution Approach 1:
The system transitions from static synchronous updates to dynamic asynchronous updates where nodes can share parameters and update models at different times based on their local training progress. This dynamic approach maintains model consistency through continuous parameter exchange while eliminating the time loss associated with waiting for all nodes to synchronize at each update step.
Solution Approach 2:
Instead of requiring continuous synchronous updates, the system implements periodic parameter sharing where nodes exchange model parameters at regular intervals or when certain training milestones are reached. This periodic action maintains sufficient model consistency across the distributed network while significantly reducing the total training time compared to strict synchronous coordination.
Data Source
AI summary
Systems and methods are provided for consensus driven learning (CDL) using machine learning (ML) to enable devices to learn a model on a data set that is distributed over several computational nodes in a decentralized manner. In an embodiment, local models are trained on local data and share model parameters in an asynchronous, decentralized, and distributed manner that imposes minimal restrictions on the topology of a communications network. Systems and methods using CDL in accordance with embodiments of the present disclosure do not require a central server to coordinate models like most conventional technologies, high bandwidth, or highly robust communication architecture between nodes.