Federated Model Update Compression for Unreliable Client Networks
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Solution Overview
Problem
Existing machine learning algorithms are inefficient in distributed environments due to high communication costs, particularly in federated learning where clients have low computational power, unstable connections, and privacy concerns, leading to expensive full model uploads.
Innovation Solution
Implement structured and sketched update techniques to reduce communication costs by restricting model updates to low-rank or sparse matrices and encoding updates before transmission, using methods like low-rank decomposition, random masking, subsampling, and quantization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If full model updates are transmitted from client to server in federated learning, then model training accuracy is maintained, but communication bandwidth consumption increases significantly
Solution Approach 1:
The patent extracts only the essential update information from the full model parameters by identifying and transmitting only the non-zero elements of the update matrix, along with their positions and values. This separation of essential information from redundant data resolves the contradiction by maintaining training accuracy while reducing communication bandwidth consumption.
Solution Approach 2:
The patent changes the parameter representation by transforming the full model update into a compressed format that includes only non-zero elements, their positions, and values. This parameter transformation enables the system to maintain the essential information needed for accurate model training while significantly reducing the quantity of data transmitted over the network.
2Reliability
If model parameters are distributed across multiple client devices, then data privacy is improved, but communication efficiency deteriorates due to unstable connections and low computational power
Solution Approach 1:
The patent extracts only the necessary update information from each client device, identifying and transmitting only the non-zero elements of the update matrix. This extraction approach reduces the communication burden on clients with limited computational power and unstable connections, thereby improving communication efficiency while maintaining data privacy through federated learning.
Solution Approach 2:
The patent applies partial action by transmitting only a subset of the full model parameters—specifically, only the non-zero elements of the update matrix. This partial transmission approach reduces communication requirements for clients with constrained resources while still providing sufficient information for effective model aggregation at the server.
3Quantity of substance
If update matrices are compressed using low-rank or sparse representations, then communication costs are reduced, but model update precision may be compromised
Solution Approach 1:
The patent creates a precise copy of the essential update information by transmitting the exact non-zero elements, their positions, and values from the update matrix. This copying approach ensures that no information is lost in compression, thereby maintaining model update precision while reducing communication costs through selective transmission of only the necessary elements.
4Reliability
If clients with limited computational power perform local model training, then data security is improved by keeping data local, but the complexity of coordinating updates across multiple clients increases
Solution Approach 1:
The patent extracts only the essential update information from each client's local training process, identifying and transmitting only the non-zero elements of the update matrix. This extraction simplifies the coordination complexity by reducing the amount of data that needs to be managed and transmitted, while clients with limited computational power can still perform local training securely with reduced computational burden.
Data Source
AI summary
The present disclosure provides efficient communication techniques for transmission of model updates within a machine learning framework, such as, for example, a federated learning framework in which a high-quality centralized model is trained on training data distributed overt a large number of clients each with unreliable network connections and low computational power. In an example federated learning setting, in each of a plurality of rounds, each client independently updates the model based on its local data and communicates the updated model back to the server, where all the client-side updates are used to update a global model. The present disclosure provides systems and methods that reduce communication costs. In particular, the present disclosure provides at least: structured update approaches in which the model update is restricted to be small and sketched update approaches in which the model update is compressed before sending to the server.