Hybrid FedGraph: An efficient hybrid federated learning algorithm using graph convolutional neural network
This work addresses hybrid data distributions in federated learning, which is incremental as it builds on existing methods for horizontal and vertical schemes.
The paper tackles the problem of hybrid federated learning, which is common in real-world scenarios but less studied, by proposing FedGraph, a generalized algorithm that uses graph convolutional neural networks to capture feature-sharing information and includes a clustering method for aggregation while preserving privacy.
Federated learning is an emerging paradigm for decentralized training of machine learning models on distributed clients, without revealing the data to the central server. Most existing works have focused on horizontal or vertical data distributions, where each client possesses different samples with shared features, or each client fully shares only sample indices, respectively. However, the hybrid scheme is much less studied, even though it is much more common in the real world. Therefore, in this paper, we propose a generalized algorithm, FedGraph, that introduces a graph convolutional neural network to capture feature-sharing information while learning features from a subset of clients. We also develop a simple but effective clustering algorithm that aggregates features produced by the deep neural networks of each client while preserving data privacy.