针对异构设备的联邦学习，动态分配模型并自适应压缩通信量。
Resource-Aware Aggregation and Sparsification in Heterogeneous Ensemble Federated Learning
- 按设备算力分配不同数量的全局模型，实现资源感知聚合。
- 动态调整稀疏率，降低深度集成训练负担，提升收敛稳定性。
- 适合边缘计算、移动设备等资源受限场景下的联邦学习应用。
联邦学习（FL）支持私有数据的分布式训练，但在实际通信场景中，系统异构性会阻碍其收敛。现有方法多采用全局剪枝或集成蒸馏应对异构性，却常忽略通信效率的实际约束。尽管深度集成能通过聚合独立训练模型的预测来提升性能，但现有集成式联邦学习方法未能充分捕捉模型预测的多样性。本文提出面向异构计算能力客户端的全局集成式联邦学习框架SHEFL。根据客户端可用资源分配不同数量的全局模型，并引入新型聚合机制，缓解客户端间的训练偏差，动态调整各客户端的稀疏化比例以降低深度集成的计算开销。大量实验表明，该方法有效缓解计算异构性，相比现有方法显著提升准确率与稳定性。
原文摘要 · Abstract (English)
Federated learning (FL) enables distributed training with private client data, but its convergence is hindered by system heterogeneity under realistic communication scenarios. Most FL schemes addressing system heterogeneity utilize global pruning or ensemble distillation, yet often overlook typical constraints required for communication efficiency. Meanwhile, deep ensembles can aggregate predictions from individually trained models to improve performance, but current ensemble-based FL methods fall short in fully capturing diversity of model predictions. In this work, we propose \textbf{SHEFL}, a global ensemble-based FL framework suited for clients with diverse computational capacities. We allocate different numbers of global models to clients based on their available resources. We introduce a novel aggregation scheme that mitigates the training bias between clients and dynamically adjusts the sparsification ratio across clients to reduce the computational burden of training deep ensembles. Extensive experiments demonstrate that our method effectively addresses computational heterogeneity, significantly improving accuracy and stability compared to existing approaches.
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