A Theoretical Perspective on Differentially Private Federated Multi-task Learning
This work addresses privacy and utility challenges in collaborative learning for domains like healthcare or finance, but it is incremental as it builds on existing federated and multi-task learning methods.
The paper tackles the problem of improving model performance through data sharing while addressing privacy and utility concerns by proposing a differentially private federated multi-task learning method, which resolves statistical heterogeneity and provides privacy and utility guarantees with convergence proofs for various objective functions.
In the era of big data, the need to expand the amount of data through data sharing to improve model performance has become increasingly compelling. As a result, effective collaborative learning models need to be developed with respect to both privacy and utility concerns. In this work, we propose a new federated multi-task learning method for effective parameter transfer with differential privacy to protect gradients at the client level. Specifically, the lower layers of the networks are shared across all clients to capture transferable feature representation, while top layers of the network are task-specific for on-client personalization. Our proposed algorithm naturally resolves the statistical heterogeneity problem in federated networks. We are, to the best of knowledge, the first to provide both privacy and utility guarantees for such a proposed federated algorithm. The convergences are proved for the cases with Lipschitz smooth objective functions under the non-convex, convex, and strongly convex settings. Empirical experiment results on different datasets have been conducted to demonstrate the effectiveness of the proposed algorithm and verify the implications of the theoretical findings.