FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout

• URL: http://arxiv.org/abs/2102.13451v2
• Date: Mon, 1 Mar 2021 09:16:03 GMT
• Title: FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout
• Authors: Samuel Horvath, Stefanos Laskaridis, Mario Almeida, Ilias Leontiadis, Stylianos I. Venieris and Nicholas D. Lane
• Abstract summary: We introduce Ordered Dropout, a mechanism that achieves an ordered, nested representation of knowledge in Neural Networks. We employ this technique, along with a self-distillation methodology, in the realm of Federated Learning in a framework called FjORD. FjORD consistently leads to significant performance gains over state-of-the-art baselines, while maintaining its nested structure.
• Score: 16.250862114257277
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 Neural Networks 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.

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