Model-Agnostic Federated Learning for Privacy-Preserving Systems

This study presents an innovative aggregation scheme for model-Agnostic, local, heterogeneous data models within the domain of Federated Learning. The proposed approach imposes minimal constraints on local models, only necessitating local model parameters and distances from local data centroids for a particular query. These requirements facilitate the design of privacy-preserving learning systems. We introduce a system architecture based on federated interpolation to operationalize the proposed scheme. The accuracy of our proposed scheme is evaluated using two distinct real-world datasets. We compare our results to the extreme case of a single-client scenario having complete access to all data points. Our findings indicate that, on average, federated interpolation maintains robust accuracy, experiencing a slight reduction of less than 10% compared to the single-client model with full data access.