Adaptive Decentralized Federated Gossip Learning for Resource-Constrained IoT Devices

Author:

Wulfert Lars1ORCID,Asadi Navidreza2ORCID,Chung Wen-Yu2ORCID,Wiede Christian1ORCID,Grabmaier Anton3ORCID

Affiliation:

1. Fraunhofer IMS, Duisburg, Germany

2. Technical University of Munich, Munich, Germany

3. University of Duisburg-Essen, Duisburg, Germany

Publisher

ACM

Reference38 articles.

1. Decentralized Aggregation for Energy-Efficient Federated Learning via D2D Communications

2. Machine Learning at the Wireless Edge: Distributed Stochastic Gradient Descent Over-the-Air

3. H. Brendan McMahan , Eider Moore , Daniel Ramage , Seth Hampson , and Blaise Agüera y Arcas . 2017 . Communication-efficient learning of deep networks from decentralized data . In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, AISTATS 2017. 1273--1282 . H. Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Agüera y Arcas. 2017. Communication-efficient learning of deep networks from decentralized data. In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, AISTATS 2017. 1273--1282.

4. Yuyang Deng , Mohammad Mahdi Kamani, and Mehrdad Mahdavi . 2020 . Adaptive Personalized Federated Learning. CoRR abs/2003.13461 (2020). arXiv:2003.13461 https://arxiv.org/abs/2003.13461 Yuyang Deng, Mohammad Mahdi Kamani, and Mehrdad Mahdavi. 2020. Adaptive Personalized Federated Learning. CoRR abs/2003.13461 (2020). arXiv:2003.13461 https://arxiv.org/abs/2003.13461

5. Topology Construction with Minimum Total Time for Geo-Distributed Decentralized Federated Learning

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