三.设计分析 经过源码的分析,我们很容易发现,FedGraphNN的模块化设计十分精巧,任一模块的可扩展性都极强。如果需要换GNN的模型,只需要更换model模块;如果需要使用不同的联邦学习算法,可以更换Aggregator模块;如果需要对模型的处理有别的操作,可以对handler模块中的具体方法进行扩展;如果需要使用不同的通信协议,将底层的...
Economist Ed Yardeni is credited with developing the Fed model in its current form in 1999, but a graph showing the relationship between long-term Treasurybond yieldsandearnings yieldsfrom 1982 to 1997 was published two years earlier in the Fed's Humphrey-Hawkins Report. The Fed model today dic...
FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networksarxiv.org/abs/2104.07145 代码地址: FedGraphNN: A Federated Learning Benchmark System for Graph Neural Networksgithub.com/FedML-AI/FedGraphNN 分支领域: Machine Learning (cs.LG); Artificial Intelligence (cs.AI);...
The name "Fed model" was manufactured by Wall Street professionals in the late 1990s. The Fed's Humphrey-Hawkins Report introduced a graph of the close relationship between long-term Treasury yields and the forward earnings yield of the S&P 500 on July 22, 1997. It covered the years from 1...
Graphs are widely used to model relational data. As graphs are getting larger and larger in real-world scenarios, there is a trend to store and compute subgraphs in multiple local systems. For example, recently proposed \emph{subgraph federated learning} methods train Graph Neural Networks (GNNs...
Few-shot model agnostic federated learning. In ACM MM, 2022. 1 [16] Wenke Huang, Guancheng Wan, Mang Ye, and Bo Du. Fed- erated graph semantic and structural learning. In IJCAI, 2023. [17] Wenke Huang, Mang Ye, Zekun Shi, and Bo Du. Generaliz- able heterogen...
looking for the old supergraph-demo-fed2? It's overhere! Federation 2 is an evolution of the original Apollo Federation with an improved shared ownership model, enhanced type merging, and cleaner syntax for a smoother developer experience. It’s backwards compatible, requiring no major changes ...
Node-level FedGraphNN:每个客户端持有一个或多个节点的 ego-networks,其中典型的任务是节点分类和链接预测。现实世界中的场景包括社会网络、传感器网络等,其中每个节点只看到其 k-hop 邻居和他们在大图中的连接 FedGraphNN 现在所支持的 GNN 和 FL 算法如下: GNN Model:GCN、GAT、GraphSage、SGC、GIN(PyTorch Geo...
3.3.2 Global Pseudo Graph Utilization 基于中心服务器生成的伪图数据,对每个本地客户端的图结构数据进行增强,表示如下: \hat{A}_k = \hat{A}_k + \beta\overline{D}^{-\frac{1}{2}}\overline{A}^{(k)}\overline{D}^{-\frac{1}{2}}\\ 3.3.3 Global Model 服务器聚合不同客户端上传的模型...
Subgraph federated learning (subgraph-FL) is a new distributed paradigm that facilitates the collaborative training of graph neural networks (GNNs) by multi-client subgraphs. Unfortunately, a significant challenge of subgraph-FL arises from subgraph heterogeneity, which stems from node and topology ...