Differentially-Private Decentralized Learning in Heterogeneous Multicast Networks
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916970814767104 |
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| author | Ziaeddini, Amir Yakimenka, Yauhen Kliewer, Jörg |
| author_facet | Ziaeddini, Amir Yakimenka, Yauhen Kliewer, Jörg |
| contents | We propose a power-controlled differentially private decentralized learning algorithm designed for a set of clients aiming to collaboratively train a common learning model. The network is characterized by a row-stochastic adjacency matrix, which reflects different channel gains between the clients. In our privacy-preserving approach, both the transmit power for model updates and the level of injected Gaussian noise are jointly controlled to satisfy a given privacy and energy budget. We show that our proposed algorithm achieves a convergence rate of O(log T), where T is the horizon bound in the regret function. Furthermore, our numerical results confirm that our proposed algorithm outperforms existing works. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_21688 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Differentially-Private Decentralized Learning in Heterogeneous Multicast Networks Ziaeddini, Amir Yakimenka, Yauhen Kliewer, Jörg Information Theory We propose a power-controlled differentially private decentralized learning algorithm designed for a set of clients aiming to collaboratively train a common learning model. The network is characterized by a row-stochastic adjacency matrix, which reflects different channel gains between the clients. In our privacy-preserving approach, both the transmit power for model updates and the level of injected Gaussian noise are jointly controlled to satisfy a given privacy and energy budget. We show that our proposed algorithm achieves a convergence rate of O(log T), where T is the horizon bound in the regret function. Furthermore, our numerical results confirm that our proposed algorithm outperforms existing works. |
| title | Differentially-Private Decentralized Learning in Heterogeneous Multicast Networks |
| topic | Information Theory |
| url | https://arxiv.org/abs/2509.21688 |