论文标题
与隐藏表示共享的联合学习图
Graph Federated Learning with Hidden Representation Sharing
论文作者
论文摘要
当每个客户端的本地数据不足时，在图形上学习（日志）被广泛用于多客户系统，并且多个客户必须共享其原始数据以学习质量高质量的模型。一种情况是向具有有限历史数据的客户推荐项目，并与社交网络中的其他客户共享类似的偏好。另一方面，由于对客户数据隐私的保护的需求不断增长，联合学习（FL）已被广泛采用：FL要求在多客户系统中对模型进行培训，并限制客户之间的原始数据共享。日志和FL之间的潜在潜在数据共享冲突尚未探索，并且如何从双方受益是一个有前途的问题。在这项工作中，我们首先制定了联合学习的图（GFL）问题，该问题统一了多客户系统中的日志和FL，然后提出共享隐藏的表示形式，而不是邻居的原始数据，以保护数据隐私作为解决方案。为了克服GFL中的偏置梯度问题，我们提供了一种梯度估计方法及其在非凸目标下的收敛分析。在实验中，我们在图表上评估了分类任务的方法。我们的实验显示了我们的理论与实践之间的良好匹配。
Learning on Graphs (LoG) is widely used in multi-client systems when each client has insufficient local data, and multiple clients have to share their raw data to learn a model of good quality. One scenario is to recommend items to clients with limited historical data and sharing similar preferences with other clients in a social network. On the other hand, due to the increasing demands for the protection of clients' data privacy, Federated Learning (FL) has been widely adopted: FL requires models to be trained in a multi-client system and restricts sharing of raw data among clients. The underlying potential data-sharing conflict between LoG and FL is under-explored and how to benefit from both sides is a promising problem. In this work, we first formulate the Graph Federated Learning (GFL) problem that unifies LoG and FL in multi-client systems and then propose sharing hidden representation instead of the raw data of neighbors to protect data privacy as a solution. To overcome the biased gradient problem in GFL, we provide a gradient estimation method and its convergence analysis under the non-convex objective. In experiments, we evaluate our method in classification tasks on graphs. Our experiment shows a good match between our theory and the practice.
