Echo-CGC: A Communication-Efficient Byzantine-Tolerant Distributed Machine Learning Algorithm in Single-Hop Radio Network ...
In the past few years, many Byzantine-tolerant distributed machine learning (DML) algorithms have been proposed in the point-to-point communication model. In this paper, we focus on a popular DML framework - the parameter server computation paradigm and iterative learning algorithms that proceed in...
| Main Authors: | , |
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| Format: | Conference Object |
| Language: | English |
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Schloss Dagstuhl – Leibniz-Zentrum für Informatik
2021
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| Subjects: | |
| Online Access: | https://dx.doi.org/10.4230/lipics.opodis.2020.7 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.OPODIS.2020.7 |
| Summary: | In the past few years, many Byzantine-tolerant distributed machine learning (DML) algorithms have been proposed in the point-to-point communication model. In this paper, we focus on a popular DML framework - the parameter server computation paradigm and iterative learning algorithms that proceed in rounds, e.g., [Gupta and Vaidya, 2020; El-Mhamdi et al., 2020; Chen et al., 2017]. One limitation of prior algorithms in this domain is the high communication complexity. All the Byzantine-tolerant DML algorithms that we are aware of need to send n d-dimensional vectors from worker nodes to the parameter server in each round, where n is the number of workers and d is the number of dimensions of the feature space (which may be in the order of millions). In a wireless network, power consumption is proportional to the number of bits transmitted. Consequently, it is extremely difficult, if not impossible, to deploy these algorithms in power-limited wireless devices. Motivated by this observation, we aim to reduce the ... : LIPIcs, Vol. 184, 24th International Conference on Principles of Distributed Systems (OPODIS 2020), pages 7:1-7:16 ... |
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