通过联合学习图结构与修复模型参数，提升噪声通信下的联邦学习鲁棒性。
Joint Graph Estimation and Signal Restoration for Robust Federated Learning
- 构建客户端参数间的图关系，同步修复缺失和噪声数据。
- 在有偏数据和噪声环境下，准确率提升2%~5%。
- 适合通信质量差或数据不均衡的联邦学习场景。
我们提出一种针对噪声通信下的联邦学习（FL）模型参数聚合的鲁棒方法。FL是一种分布式机器学习范式，中央服务器从多个客户端聚合本地模型参数。这些参数在数据收集、训练及客户端与服务器间通信过程中常出现噪声和/或缺失值，导致模型准确率显著下降。为解决此问题，我们学习一个反映客户端模型参数间成对关系的图结构，并通过联合图学习与信号（即模型参数）恢复的问题来实现。该问题被建模为差分凸（DC）优化，并利用近端DC算法高效求解。在MNIST和CIFAR-10数据集上的实验表明，所提方法在有偏数据分布和噪声条件下，分类准确率相比现有方法提升2%~5%。
原文摘要 · Abstract (English)
We propose a robust aggregation method for model parameters in federated learning (FL) under noisy communications. FL is a distributed machine learning paradigm in which a central server aggregates local model parameters from multiple clients. These parameters are often noisy and/or have missing values during data collection, training, and communication between the clients and server. This may cause a considerable drop in model accuracy. To address this issue, we learn a graph that represents pairwise relationships between model parameters of the clients during aggregation. We realize it with a joint problem of graph learning and signal (i.e., model parameters) restoration. The problem is formulated as a difference-of-convex (DC) optimization, which is efficiently solved via a proximal DC algorithm. Experimental results on MNIST and CIFAR-10 datasets show that the proposed method outperforms existing approaches by up to $2$--$5\%$ in classification accuracy under biased data distributions and noisy conditions.
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