Federated Learning Intermediate Layer Regularization for Data Heterogeneity
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Solution Overview
Problem
Federated learning systems face performance degradation due to heterogeneous data across edge devices, leading to slow convergence and unoptimized model performance, as large data heterogeneity occurs due to varying device characteristics and operations.
Innovation Solution
The proposed solution introduces a regularization term into the local training process using intermediate representations, calculated based on similarity between local and global models, which automatically determines the contribution of each layer to the regularization term, thereby enhancing model performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If federated learning is used to train models on distributed edge devices, then data privacy and security are preserved, but model performance degrades due to heterogeneous data across devices
Solution Approach 1:
The patent introduces intermediate layer representations as mediators between local heterogeneous data and the global model. These representations capture essential features while filtering out device-specific variations, enabling effective knowledge transfer across heterogeneous data distributions without compromising privacy
Solution Approach 2:
The patent dynamically adjusts the regularization strength parameter (lambda) based on data heterogeneity metrics. By changing this parameter, the system adapts the influence of intermediate representation regularization according to the actual data distribution, optimizing model performance across different heterogeneity levels
2Device complexity
If standard federated averaging is used for model aggregation, then implementation simplicity is maintained, but convergence speed decreases due to data heterogeneity
Solution Approach 1:
The patent performs preliminary extraction of intermediate layer representations during the local training phase before model aggregation. This preliminary action prepares standardized feature representations that facilitate faster and more effective convergence during subsequent global model updates
Solution Approach 2:
The patent implements a feedback mechanism where intermediate representation similarity between local and global models is calculated and used to adjust the regularization strength in subsequent training rounds. This feedback loop accelerates convergence by adapting the regularization based on actual representation alignment
3Manufacturing precision
If strong regularization is applied to handle data heterogeneity, then model performance improves, but training complexity increases
Solution Approach 1:
The patent applies regularization selectively to intermediate layer representations rather than uniformly across all model parameters. This localized approach focuses computational effort where it is most needed (in the feature extraction layers) while leaving other parts of the model relatively unchanged, reducing overall training complexity
Data Source
AI summary
A federated learning method performed by a server and a terminal includes transmitting, by the server, a global model to a terminal and receiving, by the server, a local parameter from the terminal. The local parameter may be determined by the terminal obtaining at least one model of a previous model and a global model, obtaining a representation of the obtained model, and updating a current model using the representation.