An Exploratory Analysis on Users' Contributions in Federated Learning

An Exploratory Analysis on Users' Contributions in Federated Learning

Huang, J. (TU Delft Data-Intensive Systems)
Talbi, Rania (INSA Lyon)
Zhao, Z. (TU Delft Data-Intensive Systems)
Boucchenak, Sara (INSA Lyon)
Chen, Lydia Y. (TU Delft Data-Intensive Systems)
Roos, S. (TU Delft Data-Intensive Systems)

2020

Federated Learning is an emerging distributed collaborative learning paradigm adopted by many of today's applications, e.g., keyboard prediction and object recognition. Its core principle is to learn from large amount of users data while preserving data privacy by design as collaborative users only need to share the machine learning models and keep data locally. The main challenge for such systems is to provide incentives to users to contribute high-quality models trained from their local data. In this paper, we aim to answer how well incentives recognize (in)accurate local models from honest and malicious users, and perceive their impacts on the model accuracy of federated learning systems. We first present a thorough survey on two contrasting perspectives: incentive mechanisms to measure the contribution of local models by honest users, and malicious users to deliberately degrade the overall model. We conduct simulation experiments to empirically demonstrate if existing contribution measurement schemes can disclose low-quality models from malicious users. Our results show there exists a clear tradeoff among measurement schemes in terms of the computational efficiency and effectiveness to distill the impact of malicious participants. We conclude this paper by discussing the research directions to design resilient contribution incentives.

Adversarial Behavior
Contribution Measurement
Federated Learning
Incentive Mechanisms

http://resolver.tudelft.nl/uuid:fd05e06f-f7a7-45ef-b04c-f798023d2ef4

https://doi.org/10.1109/TPS-ISA50397.2020.00014

IEEE

9781728185439

Proceedings - 2020 2nd IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications, TPS-ISA 2020

2nd IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications, TPS-ISA 2020, 2020-12-01 → 2020-12-03, Virtual, Atlanta, United States

Proceedings - 2020 2nd IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications, TPS-ISA 2020

Institutional Repository

conference paper

© 2020 J. Huang, Rania Talbi, Z. Zhao, Sara Boucchenak, Lydia Y. Chen, S. Roos