提出梯度筛选机制，解决异步联邦学习中过时梯度干扰问题。
AFBS:Buffer Gradient Selection in Semi-asynchronous Federated Learning
- 客户端加密发送标签分布矩阵，服务器按聚类选择高价值梯度。
- 在CIFAR-100上提升准确率4.8%，达成目标精度时间缩短75%。
- 适用于设备异构、数据差异大的真实联邦学习场景。
异步联邦学习（AFL）通过无需等待慢速客户端加速训练，但其异步特性引入梯度延迟问题，即过时梯度降低性能。现有方法采用梯度缓冲区构建半异步框架，但当缓冲区积累大量过时梯度时，盲目聚合会损害训练效果。为此，我们提出AFBS（异步联邦学习缓冲区选择），首个在保护隐私前提下实现缓冲区内部梯度筛选的算法。具体而言，客户端在训练前发送随机投影加密的标签分布矩阵，服务器基于此进行客户端聚类。训练过程中，服务器对各聚类内梯度按信息价值评分并筛选，剔除低价值梯度以优化半异步联邦学习。在高度异构系统与数据环境下大量实验表明，AFBS性能优于现有最佳方法。尤其在最具挑战性的任务CIFAR-100上，准确率最高提升4.8%，达成目标精度所需时间减少75%。
原文摘要 · Abstract (English)
Asynchronous federated learning (AFL) accelerates training by eliminating the need to wait for stragglers, but its asynchronous nature introduces gradient staleness, where outdated gradients degrade performance. Existing solutions address this issue with gradient buffers, forming a semi-asynchronous framework. However, this approach struggles when buffers accumulate numerous stale gradients, as blindly aggregating all gradients can harm training. To address this, we propose AFBS (Asynchronous FL Buffer Selection), the first algorithm to perform gradient selection within buffers while ensuring privacy protection. Specifically, the client sends the random projection encrypted label distribution matrix before training, and the server performs client clustering based on it. During training, server scores and selects gradients within each cluster based on their informational value, discarding low-value gradients to enhance semi-asynchronous federated learning. Extensive experiments in highly heterogeneous system and data environments demonstrate AFBS's superior performance compared to state-of-the-art methods. Notably, on the most challenging task, CIFAR-100, AFBS improves accuracy by up to 4.8% over the previous best algorithm and reduces the time to reach target accuracy by 75%.
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