Julia package for solving the vertical federated learning problem.

Federated learning is a training framework that allows multiple clients to collaboratively train a model without sharing data. VerFedLogistic.jl is a Julia package for solving the following the vertical federated multinomial logistic regression problem:

\[\min_{\theta_1, \dots, \theta_M} \enspace \frac{1}{N}\sum_{i=1}^N \ell\left(\theta_1, \dots, \theta_M; \{x_i, y_i\} \right),\]

where \(N\) is the number of data points, \(M\) is the number of clients, \(x_i\in\mathbb{R^d}\) is the feature vector, and \(y_i\in\mathbb{N}\) is the label. Every feature vector \(x_i\) is distributed across \(M\) clients \(\{x^m_i\in\mathbb{R}^{d^m}: m \in [M]\}\), where \(d^m\) is the feature dimension for client \(m\) such that \(\sum_{m=1}^M d^m = d\). For a wide range of models, such as linear and logistic regression, and support vector machines, the loss function has the form

\[\ell\left(\theta_1, \dots, \theta_M; \{x_i, y_i\} \right) := f\left( h_i; y_i\right)\]


\(h_i = \sum_{m=1}^M h_i^m, \enspace h_i^m = \langle \theta_m, x_i^m \rangle,\) and \(f(\cdot; y)\) is a differentiable function for any \(y\). For each client \(m\), the term \(h_i^m\) can be viewed as the client’s embedding of the local data point \(x_i^m\) and the local model \(\theta_m\). To preserve the privacy, clients are not allowed to share their local data set \(\mathcal{D}^m\) or local model \(\theta^m\) with other clients or with the server. Instead, clients can share only their local embeddings \(\{h_i^m\mid i\in[N]\}\) to the server for training.