Federated Model Training With Adaptive Cloud-Edge Reporting
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Solution Overview
Problem
The existing horizontal federated learning process is inflexible and inefficient due to strict model aggregation by edge servers, which does not align with actual network deployment scenarios, leading to slow communication and inconsistent training durations.
Innovation Solution
A method to dynamically select the reporting path of model information based on client capability information, allowing clients to report directly to the cloud server or through an edge server, optimizing the training process by considering memory, computing power, and network bandwidth.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is preprocessed and stored in a database before training, then training efficiency is improved, but server resources are consumed and privacy security is compromised
Solution Approach 1:
A trusted execution environment (TEE) is introduced as an intermediary between the client and server. The TEE creates an isolated secure computing space where data preprocessing and model training occur without exposing sensitive data to the untrusted server environment, thus enabling efficient processing while maintaining privacy security
Solution Approach 2:
The system segments the computing environment into two distinct parts: a trusted execution environment for secure data processing and model training, and an untrusted server environment for resource provision. This segmentation allows the server to provide computational resources without having access to or control over the sensitive data being processed
2Power
If data is transmitted to server for processing, then computational power is utilized, but transmission overhead and security risks increase
Solution Approach 1:
The trusted execution environment is nested within the server's computational infrastructure. This allows the system to leverage the server's powerful hardware resources while maintaining an isolated secure computing space, eliminating the need for separate data transmission and reducing transmission overhead
3Manufacturing precision
If model parameters are updated and stored centrally, then model optimization is achieved, but security vulnerabilities increase
Solution Approach 1:
The system extracts and processes model parameters within the trusted execution environment on the client side. Only encrypted or processed results are transmitted to the server for storage, removing sensitive intermediate parameters from the untrusted server environment and eliminating security vulnerabilities associated with central parameter storage
Data Source
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AI summary
This application provides a model training method and apparatus. The method includes: obtaining first capability information and second capability information of a client; determining, based on the first capability information and the second capability information, that the client reports model information to a cloud server or an edge server; and sending first indication information to the client. The first indication information indicates the client to report the model information to the cloud server or the edge server. According to the model training method and apparatus provided in this application, in a horizontal federated training method in a hybrid mode, a manner of reporting local model information by the client may be flexibly selected based on capability information of the client, so that horizontal federated training efficiency can be improved.