基于影响度自适应聚合，提升异构数据下联邦学习效果
Influence-oriented Personalized Federated Learning
- 用影响向量和矩阵建模客户端间相互影响
- 在非独立同分布数据下准确率提升5.2%以上
- 适合数据异构场景下的个性化模型训练
传统联邦学习方法通常采用固定权重聚合参数，忽略客户端间的相互影响，导致在异构数据环境下效果受限。为此，本文提出一种面向影响的联邦学习框架FedC^2I，通过量化客户端级和类别级影响，实现客户端层面的自适应参数聚合。核心思想是利用精心设计的影响向量与影响矩阵，显式建模系统内客户端间的相互影响：影响向量衡量客户端级影响，使客户端可选择性地吸收他人知识，并指导特征表示层的聚合；影响矩阵以更细粒度捕捉类别级影响，实现个性化分类器聚合。我们在非独立同分布（non-IID）设置下对FedC^2I与现有方法进行评估，结果表明该方法具有明显优势。
原文摘要 · Abstract (English)
Traditional federated learning (FL) methods often rely on fixed weighting for parameter aggregation, neglecting the mutual influence by others. Hence, their effectiveness in heterogeneous data contexts is limited. To address this problem, we propose an influence-oriented federated learning framework, namely FedC^2I, which quantitatively measures Client-level and Class-level Influence to realize adaptive parameter aggregation for each client. Our core idea is to explicitly model the inter-client influence within an FL system via the well-crafted influence vector and influence matrix. The influence vector quantifies client-level influence, enables clients to selectively acquire knowledge from others, and guides the aggregation of feature representation layers. Meanwhile, the influence matrix captures class-level influence in a more fine-grained manner to achieve personalized classifier aggregation. We evaluate the performance of FedC^2I against existing federated learning methods under non-IID settings and the results demonstrate the superiority of our method.
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