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FedOPAL: One-Shot Federated Learning via Analytic V... | AI Research

Key Takeaways

  • FedOPAL: One-Shot Federated Learning via Analytic Visual Prompt Tuning Federated learning allows multiple devices to collaborate on training a machine learni...
  • With the widespread deployment of basic models in edge intelligence, communication bandwidth has become a core bottleneck restricting the scalability of federated learning.
  • To address this contradiction, this paper proposes the FedOPAL framework.
  • FedOPAL: One-Shot Federated Learning via Analytic Visual Prompt Tuning Federated learning allows multiple devices to collaborate on training a machine learning model without sharing their private data.
  • FedOPAL: One-Shot Federated Learning via Analytic Visual Prompt Tuning
Paper AbstractExpand

With the widespread deployment of basic models in edge intelligence, communication bandwidth has become a core bottleneck restricting the scalability of federated learning. Although one-shot federated learning alleviates this problem by minimizing communication rounds, existing iterative fine-tuning or knowledge distillation methods still face challenges such as high server-side computational costs and hyperparameter sensitivity. Analytical federated learning achieves efficient gradientfree aggregation using least-squares closed-form solutions, but in environments with non-independent and identically distributed data, its static feature assumptions fail, leading to feature manifold misalignment and severely impairing model performance. To address this contradiction, this paper proposes the FedOPAL framework. This framework adapts the visual prompts as feature rectifiers, actively correcting the feature distribution of heterogeneous data to a linearly separable space by applying local proximal constraints, thereby satisfying the theoretical assumptions of analytical federated learning. Experimental results show that FedOPAL not only significantly outperforms the original analytical methods on several benchmarks, but also achieves accuracy comparable to state-of-the-art iterative methods while maintaining zero server-side training costs, providing a new engineering paradigm for efficient collaboration of large models on the edge.

FedOPAL: One-Shot Federated Learning via Analytic Visual Prompt Tuning
Federated learning allows multiple devices to collaborate on training a machine learning model without sharing their private data. However, as models grow larger, the communication required to sync these models becomes a major bottleneck. One-shot federated learning aims to solve this by completing the entire training process in a single round of communication. While existing methods often rely on complex server-side retraining or struggle when data is distributed unevenly across devices (non-IID data), FedOPAL introduces a more efficient, mathematically grounded approach to achieve high accuracy without the need for iterative server-side training.

Rectifying Data with Visual Prompts

The core challenge in one-shot federated learning is that different devices often hold data with vastly different characteristics, which confuses the model. FedOPAL addresses this by using "Visual Prompt Tuning." Instead of retraining the entire large foundation model, which is computationally expensive, the framework injects a small set of learnable tokens—visual prompts—into the model's input sequence. These prompts act as a filter, adjusting the local data features so they align with a common, linearly separable space. This ensures that the model can interpret data from different devices consistently, even when the underlying data distributions are highly heterogeneous.

Efficient Analytic Aggregation

Once the local features are rectified by these prompts, FedOPAL uses an analytical, gradient-free method to aggregate the results. Instead of performing multiple rounds of iterative fine-tuning, each device calculates local statistical information (autocorrelation and cross-correlation matrices) based on its processed data. These compact statistics are sent to a central server, which uses a closed-form mathematical solution to derive the optimal global classifier. This process eliminates the need for the server to perform heavy, iterative training, significantly reducing the computational burden on the entire system.

Performance and Stability

Experimental results demonstrate that FedOPAL outperforms existing analytical methods across various benchmarks, including CIFAR-10, CIFAR-100, and the Describable Textures Dataset. By effectively handling data heterogeneity, the framework maintains high accuracy even in challenging, non-IID environments where other methods often fail or suffer from significant performance drops. Furthermore, FedOPAL achieves results comparable to state-of-the-art iterative methods while maintaining zero server-side training costs, offering a practical and scalable paradigm for deploying large models on edge devices.

Key Considerations

While FedOPAL provides a robust solution for one-shot learning, its performance is tied to the representational power of the pre-trained foundation model used as the backbone. The framework is designed to be highly efficient, shifting the workload from iterative optimization to lightweight prompt tuning and algebraic computation. It is particularly well-suited for scenarios with limited bandwidth or unstable connections, providing a stable and efficient alternative to traditional, communication-heavy federated learning approaches.

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