  Federated systems enable collaborative training on highly heterogeneous data
through model personalization, which can be facilitated by employing multi-task
learning algorithms. However, significant variation in device computing
capabilities may result in substantial degradation in the convergence rate of
training. To accelerate the learning procedure for diverse participants in a
multi-task federated setting, more efficient and robust methods need to be
developed. In this paper, we design an efficient iterative distributed method
based on the alternating direction method of multipliers (ADMM) for support
vector machines (SVMs), which tackles federated classification and regression.
The proposed method utilizes efficient computations and model exchange in a
network of heterogeneous nodes and allows personalization of the learning model
in the presence of non-i.i.d. data. To further enhance privacy, we introduce a
random mask procedure that helps avoid data inversion. Finally, we analyze the
impact of the proposed privacy mechanisms and participant hardware and data
heterogeneity on the system performance.
