Gradient Vector Binarization for Federated Learning Privacy
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional federated learning methods face high communication overheads and privacy disclosure issues due to the sharing of high-dimensional model parameters or gradients between participants.
Innovation Solution
The method involves participants performing random binarization of local gradient vectors using a randomized algorithm that satisfies differential privacy, sending perturbed gradient vectors to a server, and receiving binary representations of aggregated results to update model parameters, reducing communication resource consumption and ensuring privacy protection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-dimensional model parameters or gradients are shared between participants in conventional federated learning, then model accuracy is improved, but communication overheads and privacy disclosure increase
Solution Approach 1:
The patent applies parameter changes by transforming high-dimensional gradient vectors into low-dimensional binary representations through random binarization. Each participant converts their gradient vector elements to binary values (e.g., -1 or 1) based on probabilistic rules, significantly reducing the communication dimensionality while preserving the essential direction information needed for model updates.
Solution Approach 2:
The patent extracts only the essential sign information from the full gradient vectors. By taking out only the binary representation (plus-minus signs) of the gradient elements and discarding the magnitude information, the system achieves communication efficiency while maintaining the critical directional guidance needed for collaborative model updating.
2Measurement precision
If high-dimensional model parameters or gradients are shared between participants in conventional federated learning, then model accuracy is improved, but privacy disclosure increases
Solution Approach 1:
The patent changes the parameter representation from precise floating-point gradient values to binary representations. This transformation preserves the sign information which indicates the direction of update, while completely removing magnitude information that could reveal sensitive data about the participant's local data distribution and model state.
Solution Approach 2:
The patent extracts only the minimal necessary information (binary signs) for collaborative learning while leaving out the sensitive magnitude information. This selective extraction allows participants to collaborate on model improvement without disclosing their local data characteristics, achieving privacy protection without sacrificing learning effectiveness.
3Loss of energy
If random binarization processing is performed on local gradient vectors, then communication resource consumption is reduced, but data validity may be compromised
Solution Approach 1:
The patent implements feedback mechanisms where participants receive the aggregated binary gradient vectors from the server and use them to update their local models. The success of this feedback loop is demonstrated by the ability to achieve consistent model improvements across multiple participants and iterations, validating that the binary representations preserve sufficient information for effective collaborative learning.
Solution Approach 2:
The patent creates a simplified copy of the gradient information in binary form that retains the essential directional characteristics. This binary copy serves as an efficient proxy for the full gradient vector, maintaining the feedback signal needed for model convergence while reducing communication requirements.
Data Source
AI summary
This specification disclose methods, apparatus, and systems for updating machine learning models. In one implementation, a method includes: determining, by a participant of a plurality of participants, a local gradient vector based on a local sample set and current model parameters, obtaining a perturbed gradient vector by performing random binarization processing on the local gradient vector based on a differential privacy algorithm, and sending the perturbed gradient vector to a server. The method further includes receiving a target gradient vector from the server. The target gradient vector is determined by performing binary representation on an aggregation result of aggregating a plurality of perturbed gradient vectors received from the plurality of participants. The method further includes updating, by the participant, the current model parameters based on the target gradient vector.