@inproceedings{oai:ipsj.ixsq.nii.ac.jp:00216195,
 author = {Naoya, Yokota and Yuko, Hara-Azumi and Naoya, Yokota and Yuko, Hara-Azumi},
 book = {Proceedings of Asia Pacific Conference on Robot IoT System Development and Platform},
 month = {Jan},
 note = {Although a Gossip Stochastic Gradient Descent (SGD) algorithm is known to be suitable for decentralized distributed machine learning, it has a non-convergence problem for heterogeneous datasets between multiple devices. In this paper, we propose a Gossip Swap SGD to address this problem by employing a weight swapping method between devices. Our evaluation demonstrated that our proposed method successfully improves higher accuracy without increasing computation load than the original Gossip SGD., Although a Gossip Stochastic Gradient Descent (SGD) algorithm is known to be suitable for decentralized distributed machine learning, it has a non-convergence problem for heterogeneous datasets between multiple devices. In this paper, we propose a Gossip Swap SGD to address this problem by employing a weight swapping method between devices. Our evaluation demonstrated that our proposed method successfully improves higher accuracy without increasing computation load than the original Gossip SGD.},
 pages = {94--95},
 publisher = {情報処理学会},
 title = {Weight Exchange in Decentralized Distributed Machine Learning for Resource-Constrained IoT Edges},
 volume = {2021},
 year = {2022}
}