تقرير
Decentralized Federated Learning: A Segmented Gossip Approach
| العنوان: | Decentralized Federated Learning: A Segmented Gossip Approach |
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| المؤلفون: | Hu, Chenghao, Jiang, Jingyan, Wang, Zhi |
| سنة النشر: | 2019 |
| المجموعة: | Computer Science Statistics |
| مصطلحات موضوعية: | Computer Science - Machine Learning, Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Networking and Internet Architecture, Statistics - Machine Learning |
| الوصف: | The emerging concern about data privacy and security has motivated the proposal of federated learning, which allows nodes to only synchronize the locally-trained models instead their own original data. Conventional federated learning architecture, inherited from the parameter server design, relies on highly centralized topologies and the assumption of large nodes-to-server bandwidths. However, in real-world federated learning scenarios the network capacities between nodes are highly uniformly distributed and smaller than that in a datacenter. It is of great challenges for conventional federated learning approaches to efficiently utilize network capacities between nodes. In this paper, we propose a model segment level decentralized federated learning to tackle this problem. In particular, we propose a segmented gossip approach, which not only makes full utilization of node-to-node bandwidth, but also has good training convergence. The experimental results show that even the training time can be highly reduced as compared to centralized federated learning. Comment: Accepted to the 1st International Workshop on Federated Machine Learning for User Privacy and Data Confidentiality (FML'19) |
| نوع الوثيقة: | Working Paper |
| الوصول الحر: | http://arxiv.org/abs/1908.07782Test |
| رقم الانضمام: | edsarx.1908.07782 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |