Reduced-Precision Neural Federated Learning for Lower Bandwidth
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Solution Overview
Problem
Federated learning of artificial intelligence models on end user devices faces challenges in preserving privacy, managing resource consumption, and optimizing communication and storage requirements, particularly due to the large size and complexity of neural networks.
Innovation Solution
Implementing reduced-precision formats, such as FP8, for neural network models on end user devices, converting between high-precision and reduced-precision formats using stochastic or nearest-neighbor rounding, and aggregating updated models to reduce communication, storage, and computational burden.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If full-precision neural network models are used for federated learning, then model accuracy is maintained, but network bandwidth consumption and storage requirements increase significantly
Solution Approach 1:
The patent applies parameter changes by converting neural network weights from full-precision floating-point format to reduced-precision quantized format. This transformation reduces the bit representation of weight parameters while maintaining acceptable model accuracy through intelligent quantization schemes that preserve critical information.
Solution Approach 2:
The patent creates a quantized copy of the original full-precision neural network model. This copied version uses reduced-precision representations of weights and activations, allowing federated learning to proceed with significantly smaller data transmissions while the server can reconstruct high-precision models when needed for aggregation.
2Measurement precision
If full-precision neural network models are used for federated learning, then model accuracy is maintained, but processing power and battery consumption increase
Solution Approach 1:
The patent transforms computational parameters from high-precision floating-point arithmetic to low-precision integer or reduced-floating-point operations. This parameter change reduces the computational complexity and energy requirements of matrix multiplications and other neural network operations performed on resource-constrained end-user devices.
3Measurement precision
If full-precision neural network models are used for federated learning, then model accuracy is maintained, but device storage requirements increase
Solution Approach 1:
The patent reduces the storage volume required for model parameters by changing their representation from full-precision floating-point numbers to compact quantized formats. This compression allows the same model functionality to be stored in significantly less space on end-user devices while maintaining acceptable inference accuracy.
4Quantity of substance
If reduced-precision formats are used for neural network models, then network bandwidth and processing requirements are reduced, but model accuracy may deteriorate
Solution Approach 1:
The patent performs preliminary quantization analysis and calibration before the actual federated learning process. This preliminary action involves analyzing the distribution of weight values and determining optimal quantization parameters (such as scale factors and zero-points) that minimize accuracy loss while achieving significant compression ratios.
Solution Approach 2:
The patent implements feedback mechanisms where quantized models are evaluated and their performance monitored. Based on this feedback, quantization parameters are adjusted and refined across federated learning rounds, allowing the system to adaptively optimize the balance between compression ratio and model accuracy.
Data Source
AI summary
An end user device receives a neural network model comprising one or more weights in a reduced-precision format. The received neural network model weights are converted from the reduced-precision format to a high-precision format in the device. The high-precision neural network model is trained using an iterative process by training the neural network in a reduced-precision format compute unit in the device and updating the converted high-precision format neural network model based on the training. The trained high-precision format neural network model is converted to the reduced-precision format to produce a trained reduced-precision format neural network model, and the trained reduced-precision format neural network model is sent to the remote server for aggregation with other trained reduced-precision format neural network models from other end user devices to generate an updated trained high-precision neural network model.