Weight Scope Alignment: A Frustratingly Easy Method for Model Merging (2408.12237v1)

Abstract: Merging models becomes a fundamental procedure in some applications that consider model efficiency and robustness. The training randomness or Non-I.I.D. data poses a huge challenge for averaging-based model fusion. Previous research efforts focus on element-wise regularization or neural permutations to enhance model averaging while overlooking weight scope variations among models, which can significantly affect merging effectiveness. In this paper, we reveal variations in weight scope under different training conditions, shedding light on its influence on model merging. Fortunately, the parameters in each layer basically follow the Gaussian distribution, which inspires a novel and simple regularization approach named Weight Scope Alignment (WSA). It contains two key components: 1) leveraging a target weight scope to guide the model training process for ensuring weight scope matching in the subsequent model merging. 2) fusing the weight scope of two or more models into a unified one for multi-stage model fusion. We extend the WSA regularization to two different scenarios, including Mode Connectivity and Federated Learning. Abundant experimental studies validate the effectiveness of our approach.

• The paper introduces the Weight Scope Alignment (WSA) method that aligns weight scopes to simplify merging machine learning models.
• The technique uses target weight scope guidance and unified weight scope fusion to address challenges in mode connectivity and federated learning.
• Experimental results demonstrate that WSA significantly improves merging performance, enhancing robustness even in non-I.I.D data scenarios.

The paper "Weight Scope Alignment: A Frustratingly Easy Method for Model Merging" explores an innovative technique for merging machine learning models, focusing on scenarios where model efficiency and robustness are crucial. The authors identify a significant challenge in model averaging methods, particularly due to random training processes and non-I.I.D. (Independent and Identically Distributed) data, which complicates traditional averaging-based model fusion.

The central contribution of the paper is the introduction of the Weight Scope Alignment (WSA) method. The authors observe that variations in "weight scope" across different models can heavily impact the success of merging operations. They point out that parameters of neural network layers tend to follow a Gaussian distribution, which they leverage to create their regularization approach.

WSA consists of two primary components:

1. Target Weight Scope Guidance: During model training, a target weight scope is used to align the training process. This ensures that models will have matching weight scopes, facilitating more successful merging.
2. Unified Weight Scope Fusion: This involves consolidating the weight scopes of two or more models into a single unified scope, streamlining the merging process in multi-stage model fusion settings.

To test their method, the authors apply WSA in two distinct contexts: Mode Connectivity and Federated Learning. These are challenging scenarios where models trained independently need to be integrated effectively.

Mode Connectivity involves finding paths in the parameter space that connect different models while maintaining similar performances, thus exploiting the potential to merge models through these connections. Federated Learning, on the other hand, involves training models across distributed nodes with potentially diverse data distributions, requiring innovative merging solutions to build a cohesive global model.

The authors conducted extensive experiments to demonstrate the effectiveness of WSA. The experimental results validate their claims, showing that WSA significantly improves the merging performance, making it a suitable addition to the toolbox for efficient and robust model fusion.

Overall, the paper provides compelling insights into the role of weight distribution characteristics in model merging, offering a simple yet powerful regularization technique that enhances model integration across various applications.