Efficient Client Contribution Evaluation for Horizontal Federated Learning
Abstract
In federated learning (FL), fair and accurate measurement of the contribution of each federated participant is of great significance. The level of contribution not only provides a rational metric for distributing financial benefits among federated participants, but also helps to discover malicious participants that try to poison the FL framework. Previous methods for contribution measurement were based on enumeration over possible combination of federated participants. Their computation costs increase drastically with the number of participants or feature dimensions, making them inapplicable in practical situations. In this paper an efficient method is proposed to evaluate the contributions of federated participants. This paper focuses on the horizontal FL framework, where client servers calculate parameter gradients over their local data, and upload the gradients to the central server. Before aggregating the client gradients, the central server train a data value estimator of the gradients using reinforcement learning techniques. As shown by experimental results, the proposed method consistently outperforms the conventional leave-one-out method in terms of valuation authenticity as well as time complexity.
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DNN-based algorithms called convolutional neural networks (CNNs) are used for COVID-19 detection using feature extraction from a patient chest X-ray.
“In most cases, DNN based algorithms called convolutional neural networks (CNNs) are used for COVID-19 detection using feature extraction from a patient chest X-ray [76].”
From Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey (2023) · cites this paper as [76]
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Self-citation
- Authors
- Zhao, J., Zhu, X., Wang, J., & Xiao, J.
- Affiliations
- Ping An (China) (CN)
- Hainan University (CN)
- Venue
- ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
- Publisher
- IEEE
- Published
- 2021-06-06
- Pages
- 3060-3064
- ISSN
- 1520-6149 (ISSN-L)
- OpenAlex
- W3163048467
- Cited by
- 50
- References
- 22
- Citations by year
- 2026: 3 · 2025: 12 · 2024: 16 · 2023: 5 · 2022: 12 · 2021: 2
- FWCI
- 4.04
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE International Conference on Acoustics Speech and Signal Processing (publishedVersion)
- Funders
- National Key Research and Development Program of China
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Keywords
- horizontal federated learning, reinforcement learning
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