AdaFed: Fair Federated Learning via Adaptive Common Descent Direction
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arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866909067573723136 |
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| author | Hamidi, Shayan Mohajer Yang, En-Hui |
| author_facet | Hamidi, Shayan Mohajer Yang, En-Hui |
| contents | Federated learning (FL) is a promising technology via which some edge devices/clients collaboratively train a machine learning model orchestrated by a server. Learning an unfair model is known as a critical problem in federated learning, where the trained model may unfairly advantage or disadvantage some of the devices. To tackle this problem, in this work, we propose AdaFed. The goal of AdaFed is to find an updating direction for the server along which (i) all the clients' loss functions are decreasing; and (ii) more importantly, the loss functions for the clients with larger values decrease with a higher rate. AdaFed adaptively tunes this common direction based on the values of local gradients and loss functions. We validate the effectiveness of AdaFed on a suite of federated datasets, and demonstrate that AdaFed outperforms state-of-the-art fair FL methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_04993 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AdaFed: Fair Federated Learning via Adaptive Common Descent Direction Hamidi, Shayan Mohajer Yang, En-Hui Machine Learning Artificial Intelligence Federated learning (FL) is a promising technology via which some edge devices/clients collaboratively train a machine learning model orchestrated by a server. Learning an unfair model is known as a critical problem in federated learning, where the trained model may unfairly advantage or disadvantage some of the devices. To tackle this problem, in this work, we propose AdaFed. The goal of AdaFed is to find an updating direction for the server along which (i) all the clients' loss functions are decreasing; and (ii) more importantly, the loss functions for the clients with larger values decrease with a higher rate. AdaFed adaptively tunes this common direction based on the values of local gradients and loss functions. We validate the effectiveness of AdaFed on a suite of federated datasets, and demonstrate that AdaFed outperforms state-of-the-art fair FL methods. |
| title | AdaFed: Fair Federated Learning via Adaptive Common Descent Direction |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2401.04993 |