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A Contribution-Based Device Selection Scheme in Federated Learning
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DOI：10.1109/LCOMM.2022.3181678.png)
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摘要
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In a Federated Learning (FL) setup, a number of devices contribute to the training of a common model. We present a method for selecting the devices that provide updates in order to achieve improved generalization, fast convergence, and better device-level performance. We formulate a min-max optimization problem and decompose it into a primal-dual setup, where the duality gap is used to quantify the device-level performance. Our strategy combines exploration of data freshness through a random device selection with exploitation through simplified estimates of device contributions. This improves the performance of the trained model both in terms of generalization and personalization. A modified Truncated Monte-Carlo (TMC) method is applied during the exploitation phase to estimate the device's contribution and lower the communication overhead. The experimental results show that the proposed approach has a competitive performance, with lower communication overhead and competitive personalization performance against the baseline schemes.
Keyword:
Performance evaluation
Training
Data models
Computational modeling
Convergence
Costs
Servers
Federated learning
device selection
exploration
exploitation
personalization
generalization
期刊
IF:
4.4
论文数:
1.3W
被引数:
2.2W
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