Federated Model Heterogeneous Matryoshka Representation Learning (2406.00488v1)

Abstract: Model heterogeneous federated learning (MHeteroFL) enables FL clients to collaboratively train models with heterogeneous structures in a distributed fashion. However, existing MHeteroFL methods rely on training loss to transfer knowledge between the client model and the server model, resulting in limited knowledge exchange. To address this limitation, we propose the Federated model heterogeneous Matryoshka Representation Learning (FedMRL) approach for supervised learning tasks. It adds an auxiliary small homogeneous model shared by clients with heterogeneous local models. (1) The generalized and personalized representations extracted by the two models' feature extractors are fused by a personalized lightweight representation projector. This step enables representation fusion to adapt to local data distribution. (2) The fused representation is then used to construct Matryoshka representations with multi-dimensional and multi-granular embedded representations learned by the global homogeneous model header and the local heterogeneous model header. This step facilitates multi-perspective representation learning and improves model learning capability. Theoretical analysis shows that FedMRL achieves a $O(1/T)$ non-convex convergence rate. Extensive experiments on benchmark datasets demonstrate its superior model accuracy with low communication and computational costs compared to seven state-of-the-art baselines. It achieves up to 8.48% and 24.94% accuracy improvement compared with the state-of-the-art and the best same-category baseline, respectively.

Federated Model Heterogeneous Matryoshka Representation Learning: An Expert Overview

The paper "Federated Model Heterogeneous Matryoshka Representation Learning" addresses critical challenges in the domain of federated learning (FL) by introducing an innovative approach to model training amidst heterogeneity across data, systems, and models. The framework proposed, referred to as FedMRL, aims to enhance the efficacy of model heterogeneous federated learning (MHeteroFL) by improving knowledge transfer and adaptive learning capabilities.

Key Contributions

The primary contributions of this work lie in the development of a federated learning method that integrates Matryoshka Representation Learning (MRL) to enable more robust knowledge interaction between heterogeneous client models and a globally shared homogeneous model. Key design elements include:

1. Adaptive Representation Fusion: The framework leverages a personalized representation projector that adapts to local non-independent and identically distributed (non-IID) data distributions. This feature enables the seamless integration of generalized and personalized representations, thus enhancing model personalization.
2. Multi-Granular Representation Learning: Utilizing Matryoshka Representations, the approach constructs multi-dimensional and multi-granular embedded representations. This method allows for multi-perspective learning, which in turn improves the overall learning capacity of the models.
3. Theoretical Assurance of Convergence: The authors provide a theoretical analysis demonstrating that FedMRL achieves a $\mathcal{O}(1/T)$ non-convex convergence rate, assuring that the method is capable of converging efficiently over time.
4. Experimental Validation: The empirical results showcased in the paper demonstrate that FedMRL significantly outperforms existing state-of-the-art MHeteroFL methods. Notably, it offers up to 8.48% and 24.94% improvements in accuracy compared to the best baseline and best same-category baseline, respectively.

Implications and Future Directions

Practical Implications

The FedMRL framework addresses significant practical challenges in federated learning:

• Privacy Preservation: By maintaining proprietary local models as black boxes and only transmitting a global small model, the approach significantly mitigates privacy concerns regarding sensitive client data and model structures.
• Efficiency: The reduction in communication and computational overheads, facilitated by efficient knowledge transfer mechanisms and reduced-size global models, makes FedMRL practical for deployment in resource-limited environments.

Theoretical Implications

From a theoretical perspective, FedMRL contributes to the understanding and development of adaptive learning systems within federated frameworks. By addressing model heterogeneity through adaptable representation learning, this work paves the way for further research into personalized federated frameworks and their capacities to handle diverse datasets and model architectures.

Future Research Directions

Future work may explore:

• Optimization of Representation Learning: Further refinement of the Matryoshka Representation Learning technique to optimize trade-offs among performance, storage, and computational costs.
• Broader Evaluation: Extending evaluations to a wider variety of datasets and real-world FL scenarios can enhance the robustness of the approach.
• Advanced Model Designs: Integration of advanced neural network architectures such as transformers into the FL paradigm may benefit from FedMRL’s adaptive features.

Conclusion

Overall, the paper offers a significant advancement in federated learning by proposing a method that not only addresses the intrinsic challenges posed by heterogeneous client models but also improves overall model learning capacity and operational efficiency. The insights and methodologies introduced through FedMRL are poised to influence ongoing research and development of adaptive federated learning systems, ultimately contributing to their successful deployment across varied application domains.