Abstract
Federated learning is designed for multiple mobile devices to collaboratively train an artificial intelligence model while preserving data privacy. Instead of collecting the raw training data from mobile devices to the central server, federated learning coordinates a group of devices to train a shared model in a distributed manner with their local data. However, prior to effectively deploying federated learning on resource-constrained mobile devices in large scale, different factors including the convergence rate, energy efficiency and model accuracy should be well studied. Thus, a flexible simulation framework that can be used to investigate a wide range of problems related to federated learning is urgently required.
In this paper, we propose FLSim, a framework for efficiently building simulators for federated learning. Unlike ad hoc simulators, FLSim is envisioned as an open repository of building blocks for creating simulators. To this end, FLSim consists of a set of software components organized in a well-structured software architecture that provides the foundation for maximizing flexibility and extensibility. With FLSim, creating a simulator generally involves only putting the selected components together, thus allowing users to focus on the problems being studied. We describe the design of the framework in detail and use a few use cases to demonstrate the ease with which various simulators can be constructed with FLSim.
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References
Real Time Applications of Machine Learning. https://www.redalkemi.com/blog/post/5-real-time-applications-of-machine-learning
Pytorch. https://pytorch.org
Tensorflow. https://www.tensorflow.org
Ram, A.: How smartphone apps track users and share data (2018). https://ig.ft.com/mobile-app-data-trackers/
Krizhevsky, A., Nair, V., Hinton, G.: The cifar-10 dataset. https://www.cs.toronto.edu/~kriz/cifar.html
Bishop, C.M., et al.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford (1995)
Bonawitz, K., Eichner, H., Grieskamp, W., Huba, D., Ingerman, A., Lvanov, V.: Towards federated learning at scale: system design. arXiv preprint arXiv:1902.01046 (2019)
Bonawitz, K., et al.: Practical secure aggregation for privacy-preserving machine learning. In: Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, pp. 1175–1191. ACM (2017)
Buschmann, F., Meunier, R., Hans, R., Peter, S., Michael, S.: Pattern-Oriented Software Architecture: A System of Patterns, vol. 1. Wiley, New Jersey (1996)
Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw. Pract. Experience 41(1), 23–50 (2011)
Mo, F., Haddadi, H.: Efficient and private federated learning using tee. In: EuroSys (2019)
Fowler, M.: Inversion of control containers and the dependency injection pattern (2004). https://martinfowler.com/articles/injection.html
Howell, F., McNab, R.: Simjava: a discrete event simulation library for java. Simul. Ser. 30, 51–56 (1998)
Wong, J.C.: The Cambirdge Analytica scandal changed the world, but it didn’t change Facebook (2018). https://www.theguardian.com/technology
Konečnỳ, J., McMahan, H.B., Yu, F.X., Richtárik, P., Suresh, A.T., Bacon, D.: Federated learning: strategies for improving communication efficiency. arXiv preprint arXiv:1610.05492 (2016)
Hautala, L.: Google tool lets any AI app learn without taking all your data (2018). https://www.cnet.com/news/google-ai-tool-lets-outside-apps-get-smart-without-taking-all-your-data/
Lalitha, A., Shekhar, S., Javidi, T., Koushanfar, F.: Fully decentralized federated learning. In: Third Workshop on Bayesian Deep Learning (NeurIPS) (2018)
Li, L., Xiong, H., Guo, Z., Wang, J., Xu, C.: Smartpc: hierarchical pace control in real-time federated learning system. In: 2019 IEEE Real-Time Systems Symposium (RTSS) (2019)
Hamblen, M.: Mobile users prefer Wi-Fi over cellular for lower cost, speed, and reliability (2012). https://www.computerworld.com/article/2506011/
Hamblen, M.: Google AI Blog: Federated Learning: Collaborative Machine Learning for Mobile Devices (2017). https://ai.googleblog.com/2017/04/federated-learning-collaborative.html
Marketing-Schools: Marketing Mobile Phones (2018). http://www.marketing-schools.org/consumer-psychology
McMahan, H.B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.y.: Communication-efficient learning of deep networks from decentralized data. In: Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (2017)
Halpern, S.: The champaign for mobile phone voting is getting a midterm test (2018). https://www.newyorker.com/tech/annals-of-technology/
Smith, V., Chiang, C.K., Sanjabi, M., Talwalkar, A.: Federated multi-task learning. In: Advances in Neural Information Processing Systems, pp. 4424–4434 (2017)
Sprague, M.R., et al.: Asynchronous federated learning for geospatial applications. In: Monreale, A., et al. (eds.) ECML PKDD 2018. CCIS, vol. 967, pp. 21–28. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-14880-5_2
Wang, J., et al.: Manifold: a parallel simulation framework for multicore systems. In: 2014 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS), pp. 106–115 (2014)
Wang, J., Beu, J., Yalamanchili, S., Conte, T.: Designing configurable, modifiable and reusable components for simulation of multicore systems. In: 2012 SC Companion: High Performance Computing, Networking Storage and Analysis, pp. 472–476 (2012)
Wang, S., et al.: Adaptive federated learning in resource constrained edge computing systems. Learning, vol. 8, p. 9 (2018)
Lecun, Y., Cortes, C., Burges, J.C.: The mnist database of handwritten digits. http://yann.lecun.com/exdb/mnist/
Zhao, Y., Li, M., Lai, L., Suda, N., Civin, D., Chandra, V.: Federated learning with non-iid data. arXiv preprint arXiv:1806.00582 (2018)
Acknowledgement
This work is supported by the National Key R&D Program of China (No. 2019YFB2102100), Science and Technology Development Fund of Macao S.A.R (FDCT) under number 0015/2019/AKP, Guangdong Key R&D Project (No. 2020B010164003), Shenzhen Discipline Construction Project for Urban Computing and Data Intelligence.
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Li, L., Wang, J., Xu, C. (2021). FLSim: An Extensible and Reusable Simulation Framework for Federated Learning. In: Song, H., Jiang, D. (eds) Simulation Tools and Techniques. SIMUtools 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 369. Springer, Cham. https://doi.org/10.1007/978-3-030-72792-5_30
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DOI: https://doi.org/10.1007/978-3-030-72792-5_30
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