Federated Learning Model Alignment via Weight Permutation
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Solution Overview
Problem
Federated learning systems face challenges in aggregating model knowledge due to heterogeneous and non-IID data distributions across clients, leading to reduced efficiency, robustness, and accuracy in model convergence.
Innovation Solution
The introduction of the Rebasin technique for model alignment, which permutes the weights of one model to align with another before interpolation, allowing for refined and better-aligned model knowledge to be pooled within the same loss basin during federated learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If federated learning aggregates model knowledge from multiple clients with heterogeneous data, then the model can learn from diverse data sources without centralizing data, but the model convergence accuracy and robustness deteriorate due to non-IID data distributions
Solution Approach 1:
The patent applies preliminary action by performing model alignment through weight permutation before the model aggregation step. Each client's model weights are permuted to align with a reference model's weight structure in advance, ensuring that heterogeneous models from different clients can be effectively aggregated. This preliminary alignment operation resolves the contradiction by preparing models for accurate aggregation before the actual federated learning update occurs, thereby maintaining convergence accuracy despite data heterogeneity.
2Productivity
If federated learning performs standard model aggregation without alignment, then the training process is simple and fast, but the model knowledge aggregation efficiency and robustness are reduced due to heterogeneous data distributions
Solution Approach 1:
The patent applies parameter changes by transforming the model weight parameters through permutation operations. Specifically, the weights of each client model are permuted according to the weight structure of a reference model before aggregation. This parameter transformation aligns the heterogeneous models while maintaining the computational efficiency of the federated learning process, thus improving aggregation robustness without significantly sacrificing training speed.
3Power
If model weights are directly aggregated from different clients, then the aggregation process is computationally efficient, but the alignment of model knowledge is poor due to different weight permutations from training initialization
Solution Approach 1:
The patent introduces an intermediary mechanism by using a reference model's weight structure as a mediator for aligning client models. Instead of directly aggregating misaligned weights, the system permutes each client's weights to match the reference model's structure before aggregation. This intermediary alignment step preserves computational efficiency while preventing information loss by ensuring that corresponding weights from different clients are properly aligned during aggregation.
Data Source
AI summary
Methods and systems for training neural networks with federated learning. A portion of a server-maintained machine-learning model is transferred from a server to clients, yielding a plurality of local machine-learning models. At each client, the local models are trained with locally-stored data, including determining a respective cross entropy loss for each local models. Weights are updated for each local model, and evaluated based on a common dataset to obtain activation outputs for each layer. These are transferred to the server without transferring the locally-stored data of the clients, whereupon they are permuted according to the one respective updated weight to match a dimension of the selected client to obtain a matrix, which is sent to each client for permuting the local models based on the matrix. The permuted weights are sent to the server, whereupon they are aggregated and transferred back to the clients for updating of the local models.