⚠️ UNDER DEVELOPMENT ⚠️
gossipy
Python module for simulating gossip learning.
TODOs
Features
- Logger (partially implemented)
- Models cache [Ormandi 2013, Giaretta 2019] (partially implemented)
- Perfect matching [Ormandi 2013]
Extras
- Documentation
References
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[Ormandi 2013] Ormándi, Róbert, István Hegedüs, and Márk Jelasity. ‘Gossip Learning with Linear Models on Fully Distributed Data’. Concurrency and Computation: Practice and Experience 25, no. 4 (February 2013): 556–571. https://doi.org/10.1002/cpe.2858.
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[Danner 2018] G. Danner and M. Jelasity, 'Token Account Algorithms: The Best of the Proactive and Reactive Worlds'. In 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS), 2018, pp. 885-895. https://doi.org/10.1109/ICDCS.2018.00090.
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[Giaretta 2019] Giaretta, Lodovico, and Sarunas Girdzijauskas. 'Gossip Learning: Off the Beaten Path'. In 2019 IEEE International Conference on Big Data (Big Data), 1117–1124. Los Angeles, CA, USA: IEEE, 2019. https://doi.org/10.1109/BigData47090.2019.9006216.
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[Hegedus 2021] Hegedűs, István, Gábor Danner, and Márk Jelasity. 'Decentralized Learning Works: An Empirical Comparison of Gossip Learning and Federated Learning'. Journal of Parallel and Distributed Computing 148 (February 2021): 109–124. https://doi.org/10.1016/j.jpdc.2020.10.006.