FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout (2102.13451v5)

Abstract: Federated Learning (FL) has been gaining significant traction across different ML tasks, ranging from vision to keyboard predictions. In large-scale deployments, client heterogeneity is a fact and constitutes a primary problem for fairness, training performance and accuracy. Although significant efforts have been made into tackling statistical data heterogeneity, the diversity in the processing capabilities and network bandwidth of clients, termed as system heterogeneity, has remained largely unexplored. Current solutions either disregard a large portion of available devices or set a uniform limit on the model's capacity, restricted by the least capable participants. In this work, we introduce Ordered Dropout, a mechanism that achieves an ordered, nested representation of knowledge in deep neural networks (DNNs) and enables the extraction of lower footprint submodels without the need of retraining. We further show that for linear maps our Ordered Dropout is equivalent to SVD. We employ this technique, along with a self-distillation methodology, in the realm of FL in a framework called FjORD. FjORD alleviates the problem of client system heterogeneity by tailoring the model width to the client's capabilities. Extensive evaluation on both CNNs and RNNs across diverse modalities shows that FjORD consistently leads to significant performance gains over state-of-the-art baselines, while maintaining its nested structure.

• The paper introduces Ordered Dropout to create nested DNN representations, effectively mitigating client heterogeneity in federated learning.
• It adapts model complexity to client resources via the FjORD framework, ensuring fair participation of low-end devices while maintaining high accuracy.
• Empirical evaluations on CNNs and RNNs demonstrate consistent performance improvements under both IID and non-IID data conditions.

Fair and Accurate Federated Learning under Heterogeneous Targets with Ordered Dropout

The paper "FjORD: Fair and Accurate Federated Learning under Heterogeneous Targets with Ordered Dropout" addresses a significant challenge in the domain of Federated Learning (FL): client heterogeneity. While FL has been recognized for enabling ML models to be trained across multiple decentralized devices without sharing raw data, it faces the fundamental challenge of client heterogeneity, encompassing both statistical data heterogeneity and system-level differences such as varying network bandwidths and computational capabilities of client devices.

Key Contribution: Ordered Dropout

The central contribution of the paper is the introduction of Ordered Dropout (OD), a novel mechanism designed to mitigate the effects of client system heterogeneity. Ordered Dropout facilitates a nested representation of knowledge within a deep neural network (DNN), allowing for the creation of lower footprint submodels that do not require retraining. This is achieved by enabling the extraction of submodels through an ordered pruning scheme that does not randomly sparsify neurons or filters, but instead retains the most important elements of the model as determined by the data. Interestingly, for linear transformations, OD is mathematically equivalent to a truncated Singular Value Decomposition (SVD), thus providing a theoretical foundation for the nested model capacity reduction.

FjORD Framework

Building upon Ordered Dropout, the authors propose the FjORD framework for Federated Learning, which adapts the model architecture dynamically to the capabilities of the participating clients. FjORD effectively customizes the model's complexity according to individual client resources, allowing each client to train on a submodel that fits within its system constraints and still contribute to the global model. This adaptability not only maintains high accuracy across heterogeneous devices but also enhances fairness by preventing the exclusion of low-end devices from the FL process.

Empirical Evaluation

The paper provides extensive empirical evaluations on CNNs and RNNs across various datasets, demonstrating that FjORD consistently achieves significant performance improvements over the state-of-the-art. Notably, FjORD leverages a self-distillation approach which further enhances the feature extraction capabilities of smaller submodels without the need for additional data. Experimental results exhibit that FjORD's ordered structure facilitates consistent performance improvements in both IID and non-IID data settings.

Implications and Future Directions

FjORD's contribution extends beyond performance improvements in FL deployments. By accommodating device heterogeneity, the framework promotes inclusivity, allowing a wider range of devices to participate in model training. This approach could lead to more diverse and representative model training datasets, enhancing the applicability of FL to real-world scenarios where device diversity is a haLLMark.

The potential future work includes extending the Ordered Dropout mechanism to tackle more complex models and systems, and integrating our proposed methods with ongoing efforts in privacy-preserving FL to address the data privacy concerns comprehensively. Additionally, further research into optimizing the drop probability distributions and evaluating FjORD's scalability across even larger federated systems with dynamic client participation and dropout could yield insights into further improving the framework's robustness and efficiency.

In sum, the FjORD framework exemplifies a significant step towards making federated learning adaptable to diverse real-world deployment conditions while maintaining model performance and fairness.