根据数据差异动态分配通信资源，提升联邦学习性能
DynamicFL: Federated Learning with Dynamic Communication Resource Allocation
- 按客户端数据异质性分配不同通信资源
- 相比均匀分配，模型准确率最高提升10%
- 适合通信受限但数据差异大的实际场景
联邦学习（FL）是一种分布式机器学习框架，允许多个用户利用本地数据协同训练模型。然而，设备间本地数据的显著统计异质性常导致模型性能低于独立同分布（IID）场景。本文提出DynamicFL，研究两种主流联邦学习方法——联邦随机梯度下降（FedSGD）和联邦平均（FedAvg）——在全局模型性能与通信开销之间的权衡。该方法基于客户端数据的统计异质性，结合通信资源约束，动态分配通信资源，显著优于固定资源分配策略。实验表明，DynamicFL可使模型准确率最高提升10%，有效弥合了FedSGD与FedAvg的差距，提供了一种利用通信异质性应对数据异质性的灵活框架。
原文摘要 · Abstract (English)
Federated Learning (FL) is a collaborative machine learning framework that allows multiple users to train models utilizing their local data in a distributed manner. However, considerable statistical heterogeneity in local data across devices often leads to suboptimal model performance compared with independently and identically distributed (IID) data scenarios. In this paper, we introduce DynamicFL, a new FL framework that investigates the trade-offs between global model performance and communication costs for two widely adopted FL methods: Federated Stochastic Gradient Descent (FedSGD) and Federated Averaging (FedAvg). Our approach allocates diverse communication resources to clients based on their data statistical heterogeneity, considering communication resource constraints, and attains substantial performance enhancements compared to uniform communication resource allocation. Notably, our method bridges the gap between FedSGD and FedAvg, providing a flexible framework leveraging communication heterogeneity to address statistical heterogeneity in FL. Through extensive experiments, we demonstrate that DynamicFL surpasses current state-of-the-art methods with up to a 10% increase in model accuracy, demonstrating its adaptability and effectiveness in tackling data statistical heterogeneity challenges.
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