Federated Classification in Hyperbolic Spaces via Secure Aggregation of Convex Hulls

TL;DR

This paper introduces the first federated classification method in hyperbolic spaces, utilizing convex hulls, number-theoretic label recovery, and efficient aggregation techniques, demonstrating improved accuracy on hierarchical data.

Contribution

It develops distributed hyperbolic SVM classifiers, label recovery methods, hull complexity analysis, and aggregation strategies for federated learning in hyperbolic spaces, addressing privacy and communication challenges.

Findings

Classification accuracy improved by up to 11% over Euclidean methods.

Effective privacy-preserving hyperbolic federated learning demonstrated on biological data.

New quantization and encoding methods reduce communication costs.

Abstract

Hierarchical and tree-like data sets arise in many applications, including language processing, graph data mining, phylogeny and genomics. It is known that tree-like data cannot be embedded into Euclidean spaces of finite dimension with small distortion. This problem can be mitigated through the use of hyperbolic spaces. When such data also has to be processed in a distributed and privatized setting, it becomes necessary to work with new federated learning methods tailored to hyperbolic spaces. As an initial step towards the development of the field of federated learning in hyperbolic spaces, we propose the first known approach to federated classification in hyperbolic spaces. Our contributions are as follows. First, we develop distributed versions of convex SVM classifiers for Poincar\'e discs. In this setting, the information conveyed from clients to the global classifier are convex hulls of clusters present in individual client data. Second, to avoid label switching issues, we introduce a number-theoretic approach for label recovery based on the so-called integer $B_h$ sequences. Third, we compute the complexity of the convex hulls in hyperbolic spaces to assess the extent of data leakage; at the same time, in order to limit communication cost for the hulls, we propose a new quantization method for the Poincar\'e disc coupled with Reed-Solomon-like encoding. Fourth, at the server level, we introduce a new approach for aggregating convex hulls of the clients based on balanced graph partitioning. We test our method on a collection of diverse data sets, including hierarchical single-cell RNA-seq data from different patients distributed across different repositories that have stringent privacy constraints. The classification accuracy of our method is up to $\sim 11\%$ better than its Euclidean counterpart, demonstrating the importance of privacy-preserving learning in hyperbolic spaces.