Distributed ML Node Parameter Delta Ordering
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Solution Overview
Problem
Federated learning systems face challenges in efficiently training machine learning models due to the large amount of data required for updates across distributed nodes, particularly in scenarios where IoT devices have limited network quality, leading to communication bottlenecks and resource constraints.
Innovation Solution
The method involves compressing update sizes based on communication link quality by using dense arrays of parameter deltas ordered by a reference model, allowing for adaptive reduction of parameter updates and efficient data transmission, without the need for pre-training or specific training data to select parameters for pruning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If full model updates are transmitted between distributed nodes, then model training accuracy is maintained, but communication bandwidth consumption increases and network quality requirements become stricter
Solution Approach 1:
The patent extracts only the essential information needed for model updates by transmitting parameter deltas (differences) instead of full model parameters. Each node calculates and shares only the changes in its local model parameters, significantly reducing the volume of transmitted data while preserving the essential training information needed to maintain model accuracy across the distributed system
Solution Approach 2:
The patent transforms the transmission format from absolute parameter values to relative parameter deltas. By representing updates as differences (Δθ) rather than complete parameter sets, the system reduces communication overhead while maintaining the ability to reconstruct full model states at each node, thus resolving the contradiction between data volume and training accuracy
2Productivity
If update frequency is increased to improve training convergence, then training speed improves, but network resource consumption increases
Solution Approach 1:
The patent enables more frequent update transmissions by extracting only the essential parameter delta information rather than transmitting complete model states. This extraction approach reduces each update's resource footprint, allowing the system to increase update frequency and improve convergence speed without proportionally increasing overall network resource consumption
3Ease of operation
If dense arrays of parameter deltas are used instead of sparse representations, then update application simplicity increases, but data transmission volume increases
Solution Approach 1:
The patent addresses this contradiction by ordering the dense array of parameter deltas according to a reference model structure. This ordering enables the receiving node to efficiently map deltas to the correct parameter positions without requiring complex sparse representation handling, thus maintaining ease of operation while the dense format inherently increases data size compared to sparse representations
Data Source
AI summary
A computer-implemented method for training a machine learning model in a distributed system, The distributed system comprises a plurality of nodes that exchange updates to communally train the machine learning model. Each node of the plurality of nodes maintains a local version of the machine learning model. The local version of the machine learning model of each of the plurality of nodes has been initialised with the same one or more respective parameter values. The method comprises a node: receiving an update to a local model from at least one other node in the distributed system, the local model comprising the local version of the machine learning model and the update comprising a dense array of one or more first parameter deltas, the one or more first parameter deltas being ordered in the dense array in an order determined by a reference model, each first parameter delta representing a difference between a parameter of the local model and a corresponding parameter of an updated version of the machine learning model that is maintained by the at least other node; updating the local model based on the received update and the reference model to determine an updated local model; determining one or more second parameter deltas, each second parameter delta representing a difference between a parameter of the updated local model and a corresponding parameter of a previous version of the local model; and sending an update to the at least one other node in the distributed system, wherein the update comprises a dense array of the one or more second parameter deltas, the one or more second parameter deltas being ordered in the dense array in an order determined by the reference model.