FedTMOS: efficient One-Shot Federated Learning with Tsetlin Machine

Abstract

One-Shot Federated Learning (OFL) is a promising approach that reduce communication to a single round, minimizing latency and resource consumption. However, existing OFL methods often rely on Knowledge Distillation, which introduce server-side training, increasing latency. While neuron matching and model fusion techniques bypass server-side training, they struggle with alignment when heterogeneous data is present. To address these challenges, we proposed One-Shot Federated Learning with Tsetlin Machine (FedTMOS), a novel data-free OFL framework built upon the low-complexity and class-adaptive properties of the Tsetlin Machine. FedTMOS first clusters then reassigns class-specific weights to form models using an inter-class maximization approach, efficiently generating balanced server models without requiring additional training. Our extensive experiments demonstrate that FedTMOS significantly outperforms its ensemble counterpart by an average of
6.16%, and the leading state-of-the-art OFL baselines by
7.22% across various OFL settings. Moreover, FedTMOS achieves at least a 2.3×
reduction in upload communication costs and a 75×
reduction in server latency compared to methods requiring server-side training. These results establish FedTMOS as a highly efficient and practical solution for OFL scenarios.

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Identifiers

ORCID for Geoff V. Merrett: orcid.org/0000-0003-4980-3894

ORCID for Jonathon Hare: orcid.org/0000-0003-2921-4283

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