Provable Mutual Benefits from Federated Learning in Privacy-Sensitive Domains

Nikita Tsoy, Anna Mihalkova, Teodora N Todorova, Nikola Konstantinov
Proceedings of The 27th International Conference on Artificial Intelligence and Statistics, PMLR 238:4798-4806, 2024.

Abstract

Cross-silo federated learning (FL) allows data owners to train accurate machine learning models by benefiting from each others private datasets. Unfortunately, the model accuracy benefits of collaboration are often undermined by privacy defenses. Therefore, to incentivize client participation in privacy-sensitive domains, a FL protocol should strike a delicate balance between privacy guarantees and end-model accuracy. In this paper, we study the question of when and how a server could design a FL protocol provably beneficial for all participants. First, we provide necessary and sufficient conditions for the existence of mutually beneficial protocols in the context of mean estimation and convex stochastic optimization. We also derive protocols that maximize the total clients’ utility, given symmetric privacy preferences. Finally, we design protocols maximizing end-model accuracy and demonstrate their benefits in synthetic experiments.

Cite this Paper

BibTeX
@InProceedings{pmlr-v238-tsoy24a, title = {Provable Mutual Benefits from Federated Learning in Privacy-Sensitive Domains}, author = {Tsoy, Nikita and Mihalkova, Anna and N Todorova, Teodora and Konstantinov, Nikola}, booktitle = {Proceedings of The 27th International Conference on Artificial Intelligence and Statistics}, pages = {4798--4806}, year = {2024}, editor = {Dasgupta, Sanjoy and Mandt, Stephan and Li, Yingzhen}, volume = {238}, series = {Proceedings of Machine Learning Research}, month = {02--04 May}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v238/tsoy24a/tsoy24a.pdf}, url = {https://proceedings.mlr.press/v238/tsoy24a.html}, abstract = {Cross-silo federated learning (FL) allows data owners to train accurate machine learning models by benefiting from each others private datasets. Unfortunately, the model accuracy benefits of collaboration are often undermined by privacy defenses. Therefore, to incentivize client participation in privacy-sensitive domains, a FL protocol should strike a delicate balance between privacy guarantees and end-model accuracy. In this paper, we study the question of when and how a server could design a FL protocol provably beneficial for all participants. First, we provide necessary and sufficient conditions for the existence of mutually beneficial protocols in the context of mean estimation and convex stochastic optimization. We also derive protocols that maximize the total clients’ utility, given symmetric privacy preferences. Finally, we design protocols maximizing end-model accuracy and demonstrate their benefits in synthetic experiments.} }
Endnote
%0 Conference Paper %T Provable Mutual Benefits from Federated Learning in Privacy-Sensitive Domains %A Nikita Tsoy %A Anna Mihalkova %A Teodora N Todorova %A Nikola Konstantinov %B Proceedings of The 27th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2024 %E Sanjoy Dasgupta %E Stephan Mandt %E Yingzhen Li %F pmlr-v238-tsoy24a %I PMLR %P 4798--4806 %U https://proceedings.mlr.press/v238/tsoy24a.html %V 238 %X Cross-silo federated learning (FL) allows data owners to train accurate machine learning models by benefiting from each others private datasets. Unfortunately, the model accuracy benefits of collaboration are often undermined by privacy defenses. Therefore, to incentivize client participation in privacy-sensitive domains, a FL protocol should strike a delicate balance between privacy guarantees and end-model accuracy. In this paper, we study the question of when and how a server could design a FL protocol provably beneficial for all participants. First, we provide necessary and sufficient conditions for the existence of mutually beneficial protocols in the context of mean estimation and convex stochastic optimization. We also derive protocols that maximize the total clients’ utility, given symmetric privacy preferences. Finally, we design protocols maximizing end-model accuracy and demonstrate their benefits in synthetic experiments.
APA
Tsoy, N., Mihalkova, A., N Todorova, T. & Konstantinov, N.. (2024). Provable Mutual Benefits from Federated Learning in Privacy-Sensitive Domains. Proceedings of The 27th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 238:4798-4806 Available from https://proceedings.mlr.press/v238/tsoy24a.html.

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