Differentially-private federated learning with long-term budget constraints using online Lagrangian descent
Odeyomi, Olusola T. Záruba, Gergely V.
Odeyomi, Olusola T.
Záruba, Gergely V.
Citations
Altmetric:
Other Names
Location
Time Period
Advisors
Original Date
Digitization Date
Issue Date
2021-05-10
Type
Conference paper
Genre
Keywords
Servers,Data models,Real-time systems,Collaborative work,Computational modeling,Privacy,Differential privacy
Subjects (LCSH)
Citation
Odeyomi, O. T., & Zaruba, G. (2021). Differentially-private federated learning with long-term budget constraints using online lagrangian descent. Paper presented at the 2021 IEEE World AI IoT Congress, AIIoT 2021, 1-6. doi:10.1109/AIIoT52608.2021.9454170
Abstract
This paper addresses the problem of time-varying data distribution in a fully decentralized federated learning setting with budget constraints. Most existing work cover only fixed data distribution in the centralized setting, which is not applicable when the data becomes time-varying, such as in realtime traffic monitoring. More so, a lot of existing work do not address budget constraint problem common in practical federated learning settings. To address these problems, we propose an online Lagrangian descent algorithm. To provide privacy to the local model updates of the clients, local differential privacy is introduced. We show that our algorithm incurs the best regret bound when compared to other similar algorithms, while satisfying the budget constraints in the long term.
Table of Contents
Description
Click on the DOI link to access this conference paper at the publishers website (may not be free).
Publisher
IEEE
Journal
Book Title
Series
2021 IEEE World AI IoT Congress (AIIoT);