Smart Information Exchange for Unsupervised Federated Learning via Reinforcement Learning
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866917590769598464 |
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| author | Lee, Seohyun Das, Anindya Bijoy Wagle, Satyavrat Brinton, Christopher G. |
| author_facet | Lee, Seohyun Das, Anindya Bijoy Wagle, Satyavrat Brinton, Christopher G. |
| contents | One of the main challenges of decentralized machine learning paradigms such as Federated Learning (FL) is the presence of local non-i.i.d. datasets. Device-to-device transfers (D2D) between distributed devices has been shown to be an effective tool for dealing with this problem and robust to stragglers. In an unsupervised case, however, it is not obvious how data exchanges should take place due to the absence of labels. In this paper, we propose an approach to create an optimal graph for data transfer using Reinforcement Learning. The goal is to form links that will provide the most benefit considering the environment's constraints and improve convergence speed in an unsupervised FL environment. Numerical analysis shows the advantages in terms of convergence speed and straggler resilience of the proposed method to different available FL schemes and benchmark datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_09629 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Smart Information Exchange for Unsupervised Federated Learning via Reinforcement Learning Lee, Seohyun Das, Anindya Bijoy Wagle, Satyavrat Brinton, Christopher G. Machine Learning One of the main challenges of decentralized machine learning paradigms such as Federated Learning (FL) is the presence of local non-i.i.d. datasets. Device-to-device transfers (D2D) between distributed devices has been shown to be an effective tool for dealing with this problem and robust to stragglers. In an unsupervised case, however, it is not obvious how data exchanges should take place due to the absence of labels. In this paper, we propose an approach to create an optimal graph for data transfer using Reinforcement Learning. The goal is to form links that will provide the most benefit considering the environment's constraints and improve convergence speed in an unsupervised FL environment. Numerical analysis shows the advantages in terms of convergence speed and straggler resilience of the proposed method to different available FL schemes and benchmark datasets. |
| title | Smart Information Exchange for Unsupervised Federated Learning via Reinforcement Learning |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2402.09629 |