Federated Learning with Heterogeneous and Private Label Sets

TL;DR

This paper investigates federated learning with heterogeneous and private label sets, proposing adaptations of standard methods that preserve privacy with minimal impact on model performance.

Contribution

It introduces methods for federated learning with private label sets, demonstrating that privacy can be maintained with little accuracy loss through adapted algorithms.

Findings

Reducing client label sets harms performance significantly.

Centralized tuning improves representational alignment but increases variance.

Proposed methods perform well in private label settings, comparable to standard methods in public settings.

Abstract

Although common in real-world applications, heterogeneous client label sets are rarely investigated in federated learning (FL). Furthermore, in the cases they are, clients are assumed to be willing to share their entire label sets with other clients. Federated learning with private label sets, shared only with the central server, adds further constraints on learning algorithms and is, in general, a more difficult problem to solve. In this work, we study the effects of label set heterogeneity on model performance, comparing the public and private label settings -- when the union of label sets in the federation is known to clients and when it is not. We apply classical methods for the classifier combination problem to FL using centralized tuning, adapt common FL methods to the private label set setting, and discuss the justification of both approaches under practical assumptions. Our experiments show that reducing the number of labels available to each client harms the performance of all methods substantially. Centralized tuning of client models for representational alignment can help remedy this, but often at the cost of higher variance. Throughout, our proposed adaptations of standard FL methods perform well, showing similar performance in the private label setting as the standard methods achieve in the public setting. This shows that clients can enjoy increased privacy at little cost to model accuracy.