Adaptive Federated Learning Model Compression for Edge Devices
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Solution Overview
Problem
Current federated learning implementations face challenges with high communication signaling and resource intensive processes, particularly in devices with limited compute capabilities, leading to inefficiencies in communication, storage, and energy usage, and overlook device-specific characteristics which can limit inferencing accuracy and energy efficiency.
Innovation Solution
An adaptive federated learning approach where an aggregating device compresses model collections based on the capabilities of contributing devices, allowing them to train models locally without transmitting data, and updates the model collections dynamically based on performance metrics, enabling efficient communication and resource management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If federated learning is implemented with full model collections on all devices, then model training capability is improved, but energy consumption and communication overhead increase significantly
Solution Approach 1:
The patent applies local quality by customizing model collections for each contributing device based on its specific capabilities (compute power, memory, energy constraints). Instead of providing identical full model collections to all devices, the system tailors the model set to match each device's local characteristics, enabling effective training while respecting energy and resource limitations.
Solution Approach 2:
The patent segments the model collection into different subsets appropriate for different device capabilities. The model collection is divided and allocated selectively to contributing devices based on their abilities, so that each device receives only the portion it can effectively process, reducing unnecessary energy consumption while maintaining training capability.
2Measurement precision
If large model collections are transmitted to all contributing devices, then model training accuracy is improved, but communication signaling overhead increases
Solution Approach 1:
The patent transmits different model collections to different contributing devices based on their capabilities. Devices with higher capabilities receive more comprehensive models for better accuracy, while resource-constrained devices receive optimized subsets. This selective transmission reduces overall communication overhead while maintaining appropriate accuracy levels for each device type.
3Device complexity
If uniform model collections are provided to all devices, then system simplicity is maintained, but device-specific performance optimization is lost
Solution Approach 1:
The patent implements a dynamic model collection allocation system that adapts to the capabilities of contributing devices. The model collection is not static and identical for all devices, but dynamically adjusted based on device characteristics such as compute power, memory availability, and energy constraints. This dynamic approach optimizes energy efficiency while maintaining manageable system complexity through automated capability assessment.
4Quantity of substance
If full model collections are used on resource-constrained devices, then model completeness is improved, but resource utilization efficiency decreases
Solution Approach 1:
The patent applies partial action by providing contributing devices with model collections that are partially complete rather than fully complete. Resource-constrained devices receive optimized subsets of models that are sufficient for their capabilities, avoiding the waste of processing power and memory that would result from attempting to load and process complete model collections they cannot effectively utilize.
Data Source
AI summary
An aggregating device may be in communication with contributing devices. The aggregating device may maintain a model collection including machine learning models for performing the same type of task. The aggregating device may receive, from a contributing device, a status report including information associated with one or more resources available at the contributing device. The aggregating device may compress the model collection based on the status report and transmit the compressed model collection to the respective contributing device. The contributing device may train one or more models in the model collection using data available at the contribution device. The data used for training models may not be transmitted to the aggregating device. The aggregating device may receive, from the contributing device, information associated with an update of the one or more models. The aggregating device may update the model collection based on the information received from the contributing device.