FedMTL: Privacy-Preserving Federated Multi-Task Learning

Pritam Sen, Cristian Borcea

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Multi-task learning (MTL) enables simultaneous learning of related tasks, enhancing the generalization performance of each task and facilitating faster training and inference on resource-constrained devices. Federated Learning (FL) can further enhance performance by enabling collaboration among devices, effectively leveraging distributed data to improve model performance, while ensuring that the raw data remains on the respective devices. However, conventional FL is inadequate for handling MTL models trained on different sets of tasks. This paper proposes FedMTL, a new FL aggregation technique that handles task heterogeneity across users. FedMTL generates personalized MTL models based on task similarities, which are determined by analyzing the parameters for the task-specific layers of the trained models. To prevent privacy leakage through these model parameters and to protect the privacy of the task types, FedMTL employs low-overhead algorithms that are adaptable to existing techniques for secure aggregation. Extensive experiments on three datasets demonstrate that FedMTL performs better than state-of-the-art approaches. Additionally, we implement the FedMTL aggregation algorithm using secure multi-party computation, showing that it can achieve the same accuracy with the plain-text version while preserving privacy.

Original languageEnglish (US)
Title of host publicationECAI 2024 - 27th European Conference on Artificial Intelligence, Including 13th Conference on Prestigious Applications of Intelligent Systems, PAIS 2024, Proceedings
EditorsUlle Endriss, Francisco S. Melo, Kerstin Bach, Alberto Bugarin-Diz, Jose M. Alonso-Moral, Senen Barro, Fredrik Heintz
PublisherIOS Press BV
Pages1993-2002
Number of pages10
ISBN (Electronic)9781643685489
DOIs
StatePublished - Oct 16 2024
Event27th European Conference on Artificial Intelligence, ECAI 2024 - Santiago de Compostela, Spain
Duration: Oct 19 2024Oct 24 2024

Publication series

NameFrontiers in Artificial Intelligence and Applications
Volume392
ISSN (Print)0922-6389
ISSN (Electronic)1879-8314

Conference

Conference27th European Conference on Artificial Intelligence, ECAI 2024
Country/TerritorySpain
CitySantiago de Compostela
Period10/19/2410/24/24

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

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