Federated Learning Gradient Estimation via Forward Propagation
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Solution Overview
Problem
Federated learning (FL) faces challenges with edge devices lacking computational and storage resources for back-propagation, leading to high computational burden and white-box vulnerability, which can be addressed by implementing a back-propagation-free approach that is memory-efficient and compatible with inference-only hardware optimization.
Innovation Solution
The method involves distributing model parameters to clients for perturbation, using seeds to generate perturbations, and estimating gradients through forward processes without back-propagation, allowing for secure aggregation and reduced memory requirements, thereby facilitating training within trusted execution environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional back-propagation is used for federated learning, then model training accuracy is improved, but computational burden and memory requirements on edge devices increase significantly
Solution Approach 1:
The patent extracts the back-propagation computation from edge devices and relocates it to a server. Edge devices only perform forward propagation to generate loss values, while the server performs the computationally intensive gradient estimation and model parameter updates. This extraction resolves the contradiction by maintaining training accuracy through centralized gradient computation while significantly reducing the computational burden on resource-constrained edge devices.
Solution Approach 2:
The patent introduces loss values as an intermediary between forward propagation and gradient computation. Instead of directly computing gradients at edge devices, the system uses loss values generated by forward propagation as inputs for server-side gradient estimation. This intermediary approach enables accurate gradient computation without requiring edge devices to perform complex back-propagation operations.
2Manufacturing precision
If back-propagation is implemented on edge devices, then gradient computation is achieved, but memory resources are insufficient to store required data
Solution Approach 1:
The patent extracts the memory-intensive back-propagation data storage requirement from edge devices and relocates it to the server. Edge devices only need to store minimal data for forward propagation, while the server stores and processes all necessary data for gradient estimation. This extraction resolves the memory constraint contradiction by maintaining gradient computation capability while dramatically reducing memory requirements on edge devices.
3Adaptability or versatility
If model parameters are shared with clients for training, then federated learning functionality is enabled, but white-box vulnerability increases
Solution Approach 1:
The patent introduces loss values as an intermediary that masks the underlying model parameters. Clients receive and process model parameters to generate loss values, but the actual parameter values remain hidden behind this intermediary layer. This intermediary approach enables federated learning functionality while protecting against white-box attacks, as attackers cannot directly access or analyze the model parameters.
Solution Approach 2:
The patent uses copies of model parameters distributed to clients for training purposes, while the original parameters remain secured on the server. Clients work with these parameter copies to generate loss values, but the copies are designed to be unusable for direct model extraction or analysis. This copying approach enables distributed training functionality while maintaining security against white-box vulnerabilities.
Data Source
AI summary
A method for training a machine learning model is described, comprising receiving, for each perturbation of a plurality of perturbations of model parameters of a starting version of the machine learning model, a change of loss of the machine learning model caused by the perturbation for a set of training data determined by feeding the set of training data to one or more perturbed versions of the machine learning model, estimating a gradient of the loss of the machine learning model with respect to the model parameters from the determined changes of loss and updating the starting version of the machine learning model to an updated version of the machine learning model by changing the model parameters in a direction for which the estimated gradient indicates a reduction of loss.