动态调整每轮参与客户端数量，通信效率提升最高30%。
Communication-Efficient Federated Learning with Adaptive Number of Participants
- 根据当前条件自适应决定每轮参与客户端数
- 在多种场景下实现最高30%通信量节省
- 适合资源受限的实时联邦学习应用
深度模型的快速扩展带来了性能提升，但也引发诸多挑战。联邦学习（FL）作为去中心化训练的有前景框架，有效缓解了这些问题，但通信效率仍是关键瓶颈，尤其在客户端异构且动态参与的情况下。现有方法如FedAvg、FedProx及客户端选择策略虽能缓解通信开销，但对每轮选择多少客户端这一问题仍研究不足。本文提出智能参与者选择（ISP），一种动态确定每轮最优客户端数量的自适应机制，可在不损失模型精度的前提下提升通信效率。我们在视觉变换器、真实心电图（ECG）分类任务以及梯度压缩训练等多种设置下验证了ISP的有效性。结果表明，该方法在不同场景中均实现最高达30%的通信节省，且最终模型质量保持不变。将ISP应用于多个真实世界ECG分类任务进一步揭示：客户端数量选择是联邦学习中一个独立的关键环节。
原文摘要 · Abstract (English)
Rapid scaling of deep learning models has enabled performance gains across domains, yet it introduced several challenges. Federated Learning (FL) has emerged as a promising framework to address these concerns by enabling decentralized training. Nevertheless, communication efficiency remains a key bottleneck in FL, particularly under heterogeneous and dynamic client participation. Existing methods, such as FedAvg and FedProx, or other approaches, including client selection strategies, attempt to mitigate communication costs. However, the problem of choosing the number of clients in a training round remains extremely underexplored. We introduce Intelligent Selection of Participants (ISP), an adaptive mechanism that dynamically determines the optimal number of clients per round to enhance communication efficiency without compromising model accuracy. We validate the effectiveness of ISP across diverse setups, including vision transformers, real-world ECG classification, and training with gradient compression. Our results show consistent communication savings of up to 30\% without losing the final quality. Applying ISP to different real-world ECG classification setups highlighted the selection of the number of clients as a separate task of federated learning.
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