分层更新聚合与回收的通信高效联邦学习
Layer-wise Update Aggregation with Recycling for Communication-Efficient Federated Learning
AI总结：
FedLUAR通过分层更新聚合与回收技术，在保持模型准确性的同时显著降低联邦学习的通信成本。
摘要：
在联邦学习（FL）中，昂贵的通信成本是一个常见的性能瓶颈，使其在现实应用中吸引力不足。许多通信高效的FL方法集中在丢弃部分模型更新，主要基于梯度大小。在本研究中，我们发现回收先前的更新，而不是简单地丢弃它们，更有效减少通信成本同时保持FL性能。我们提出了FedLUAR，一种分层更新聚合与回收方案用于通信高效的FL。我们首先定义了一个有用的度量标准，该标准量化了聚合梯度对每层模型参数值的影响程度。FedLUAR根据该度量标准选择几层，并在服务器端回收它们的先前更新。我们的广泛实证研究证明，更新回收方案显著减少了通信成本同时保持模型准确性。例如，我们的方法在AG News数据集上实现了与FedAvg几乎相同的准确性，同时将通信成本降低到仅17%。
英文摘要：
Expensive communication cost is a common performance bottleneck in Federated Learning (FL), which makes it less appealing in real-world applications. Many communication-efficient FL methods focus on discarding a part of model updates mostly based on gradient magnitude. In this study, we find that recycling previous updates, rather than simply dropping them, more effectively reduces the communication cost while maintaining FL performance. We propose FedLUAR, a Layer-wise Update Aggregation with Recycling scheme for communication-efficient FL. We first define a useful metric that quantifies the extent to which the aggregated gradients influences the model parameter values in each layer. FedLUAR selects a few layers based on the metric and recycles their previous updates on the server side. Our extensive empirical study demonstrates that the update recycling scheme significantly reduces the communication cost while maintaining model accuracy. For example, our method achieves nearly the same AG News accuracy as FedAvg, while reducing the communication cost to just 17%.
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