Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learning (2106.06047v2)

Abstract: Federated learning is an emerging research paradigm enabling collaborative training of machine learning models among different organizations while keeping data private at each institution. Despite recent progress, there remain fundamental challenges such as the lack of convergence and the potential for catastrophic forgetting across real-world heterogeneous devices. In this paper, we demonstrate that self-attention-based architectures (e.g., Transformers) are more robust to distribution shifts and hence improve federated learning over heterogeneous data. Concretely, we conduct the first rigorous empirical investigation of different neural architectures across a range of federated algorithms, real-world benchmarks, and heterogeneous data splits. Our experiments show that simply replacing convolutional networks with Transformers can greatly reduce catastrophic forgetting of previous devices, accelerate convergence, and reach a better global model, especially when dealing with heterogeneous data. We release our code and pretrained models at https://github.com/Liangqiong/ViT-FL-main to encourage future exploration in robust architectures as an alternative to current research efforts on the optimization front.

• The paper demonstrates that Vision Transformers enhance federated learning performance by reducing catastrophic forgetting and accelerating convergence.
• The paper conducts extensive empirical comparisons between CNNs and Transformer architectures across diverse federated settings and benchmarks.
• The paper reveals that self-attention mechanisms in Vision Transformers boost robustness against non-IID data, ensuring improved accuracy and stability.

An Overview of "Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learning"

The paper "Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learning" explores the architectural advancements in Federated Learning (FL), suggesting that traditional choices may hinder performance, especially under data heterogeneity. The research focuses on the robustness of self-attention-based architectures, particularly Vision Transformers (ViTs), as a viable option for enhancing FL systems in heterogeneous environments.

Key Contributions and Findings

1. Evaluation of Neural Architectures: The paper conducts a comprehensive empirical analysis comparing convolutional neural networks (CNNs) and Transformer architectures under federated settings. Specifically, it examines these architectures' effectiveness across different federated algorithms, real-world benchmarks, and diverse data splits.
2. Transformer Robustness: The results show significant improvements when substituting CNNs with Vision Transformers in FL systems. Transformers demonstrated robustness against distribution shifts, which is crucial for managing non-IID (non-independent and identically distributed) data encountered in federated setups. This robustness is primarily due to the ability of self-attention mechanisms to better capture global patterns across heterogeneous data distributions.
3. Performance Metrics: Vision Transformers outperform CNNs by reducing catastrophic forgetting, accelerating convergence, and achieving superior global model performance. For instance, replacing ResNet with ViTs in the CIFAR-10 dataset experiments led to a marked improvement in accuracy and stability, especially as data heterogeneity increased.
4. Implication for Real-World Applications: The application of Transformers provides immediate improvements in federated learning without additional training heuristics or parameter tuning. Such architectures offer promising solutions for domains requiring enhanced security and collaboration, such as healthcare, where data is inherently distributed and heterogeneous.

Implications and Future Directions

The implications of this research are manifold. First, it challenges the predominance of CNNs in FL settings by highlighting alternative architectures that promise greater adaptability and robustness. As FL becomes increasingly pertinent for privacy-sensitive applications, these findings suggest a shift towards Transformer-based designs which can naturally accommodate varying data distributions without significant overhead.

The paper also sets a foundation for further exploration into architecture-driven strategies within FL, emphasizing that architectural choices are as critical as optimization techniques. For future developments, integrating Transformers with ongoing optimization advancements could yield further improvements. The intersection of these fields offers fertile ground for innovative approaches to managing complex federated systems.

In conclusion, the paper contributes to federated learning by offering evidence of the efficacy of Transformer architectures in heterogeneous settings, encouraging the FL research community to consider these insights when designing systems that can robustly and efficiently handle data variability.