Secure federated learning (FL) preserves data privacy during distributed model training. However, deploying such frameworks across heterogeneous devices results in performance bottlenecks, due to straggler clients with limited computational or network capabilities, slowing training for all participating clients. This paper introduces the first straggler mitigation technique for secure aggregation with deep neural networks. We propose CLIP, a client-side invariant neuron pruning technique coupled with network-aware pruning, that addresses compute and network bottlenecks due to stragglers during training with minimal accuracy loss. Our technique accelerates secure FL training by 13% to 34% across multiple datasets (CIFAR10, Shakespeare, FEMNIST) with an accuracy impact of between 1.3% improvement to 2.6% reduction.

翻译：安全联邦学习（FL）在分布式模型训练过程中保护数据隐私。然而，在异构设备上部署此类框架会导致性能瓶颈，这是由于计算或网络能力有限的掉队客户端拖慢了所有参与客户端的训练速度。本文提出了首个面向深度神经网络安全聚合的掉队者缓解技术。我们提出了CLIP，一种结合网络感知剪枝的客户端不变性神经元剪枝技术，该技术能以最小的精度损失解决训练过程中因掉队者引起的计算和网络瓶颈。我们的技术在多个数据集（CIFAR10、Shakespeare、FEMNIST）上将安全联邦学习训练加速了13%至34%，精度影响在提升1.3%到降低2.6%之间。